Artificial intelligence applications for computer-aided dispatch systems

ABSTRACT

Exemplary embodiments of the present invention provide a virtual dispatch assist system in which various types of Intelligent Agents are deployed (e.g., as part of a new CAD system architecture or as add-ons to existing CAD systems) to analyze vast amounts of historic operational data and provide various types of dispatch assist notifications and recommendations that can be used by a dispatcher or by the CAD system itself (e.g., autonomously) to make dispatch decisions.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This patent application claims the benefit of U.S. Provisional PatentApplication No. 62/683,754 entitled Project LAMBDA filed Jun. 12, 2018,which is hereby incorporated herein by reference in its entirety.

The subject matter of this patent application may be related to thesubject matter of commonly-owned U.S. patent application Ser. No.15/384,874 entitled COMPUTER-AIDED DISPATCH SYSTEMS AND METHOD UTILIZINGBIOMETRICS TO ASSESS RESPONDER CONDITION AND SUITABILITY filed Dec. 20,2016 and published on Jun. 21, 2018 as United States Patent ApplicationPublication No. US 2018/0174430, which is hereby incorporated herein byreference in its entirety.

FIELD OF THE INVENTION

The present invention relates to computer-aided dispatch systems, andmore particularly to computer-aided dispatch systems that leverageartificial intelligence to aid in making dispatch decisions.

BACKGROUND OF THE INVENTION

In emergency situations, time is of the essence in dispatching emergencyresponders. Not only must an emergency responder be able to arrive at anincident quickly, but the right type of emergency responder must bedispatched to deal with the special circumstances of the incident. Forexample, a hostage situation might require an emergency responder withhostage negotiation skills, while a potential drowning situation mightrequire an emergency responder with water rescue skills.

A computer-aided dispatch (CAD) system is a computer system runningspecialized software to make, or to assist a dispatcher in making,dispatch decisions for various types of assets such as, for example,personnel (e.g., police officers, firefighters, EMTs and other medicalprofessionals, couriers, field service technicians, etc.), vehicles(e.g., police vehicles, fire trucks, ambulances, med-flight helicopters,boats, livery vehicles, etc.), equipment (e.g., special equipment neededfor responding to a particular event or situation, such as “jaws oflife” for a car accident), and facilities (e.g., a hospital or othermedical care facility that can handle specific types of patients). Forconvenience, the term “unit” may be used herein to refer to one or moreemergency responders and related equipment dispatched to an incident(e.g., a police unit may include a specific police vehicle and one ormore police officers who are utilizing that police vehicle).

CAD systems typically gather and store vast amounts of information aboutthe various assets, such as, for example, status, availability,location, capabilities, and usage history. CAD systems typically alsogather and store information about each emergency incident, such as, forexample, the location and type of emergency, among others. Suchinformation may be gathered and stored automatically (e.g., through anyof a variety of computer-based communication systems) and/or by acall-taker who gathers the information and enters it into the CADsystem. The CAD system then can assist dispatchers in assigning tasksfor the emergency responders, for example, by making a recommendation ofwhich emergency personnel and/or vehicle(s) to assign to a particularemergency incident based upon criteria such as the type of emergency,the proximity of emergency responders to the emergency location, thestatus of each available emergency responder (e.g., whether or not aparticular emergency responder is currently responding to an emergencyincident), necessary equipment for the emergency incident (e.g., jaws oflife), necessary skills for the emergency incident (e.g. suicidenegotiation skills, water rescue skills, etc.), or minimal turns (forlong ladder fire trucks), among others.

After responding to an emergency incident, it is often required for aparty to be transported to a facility, such as, for example, a hospitalor detention center. Therefore, in addition to keeping track ofemergency personnel and vehicles, some CAD systems also gather and storeinformation about each of a number of available facilities, such as, forexample, the type of facility, the location of the facility, and theservices provided by the facility (e.g., general emergency care vs.specialty treatments), among others. The CAD system then can assistdispatchers in assigning an emergency responder to an emergency incidentand then to a facility, for example, by making a facility recommendationbased on criteria such as the type and severity of care needed by thepatient or victim, the capabilities of available facilities, and theproximity of available facilities to the emergency location, amongothers. For example, a dispatcher typically would prefer to assign anambulance to the closest hospital, but in some cases the CAD system mayrecommend a second hospital that is further away (e.g., the closesthospital may only provide general emergency care while the patientwithin the ambulance may require specialty treatment, such as cardiaccare, that is only provided by the second hospital that is furtheraway).

Not only can dispatch decisions be very complex, based on an enormousamount of data collected by the system (e.g., the current dispositionand condition of the various personnel, vehicles, equipment, andfacilities that are candidates for handling the situation), but alsosuch dispatch decisions generally need to be made quickly. Generallyspeaking, information from the call is entered into the CAD system usingdesignated graphical user interface screens, and the CAD system canprocess the entered information along with other information stored inthe system to make recommendations to the dispatcher. Suchrecommendations are generally based on rules specifically programmed bya programmer, e.g., the CAD system may recommend that the nearestambulance respond to the event and transport the patient to the nearesthospital. In some cases, however, the nearest ambulance may not be thebest responder (e.g., the medical personnel are not trained in handlingburn victims or the ambulance is not properly equipped to handle burnvictims), and the nearest hospital may not be the best facility to treatthe patient (e.g., the nearest hospital may be unable to take newincoming patients or may not have appropriate personnel available totreat burn victims). Furthermore, CAD systems must be able to deal withvery complex sequences of related or unrelated events, such as, forexample, a string of burglaries that occur over a number of days. Again,the CAD system can employ rule-based logic to provide recommendations tothe dispatcher.

SUMMARY OF VARIOUS EMBODIMENTS

In accordance with one embodiment of the invention, a computer-aideddispatch (CAD) system comprises a CAD database storing CAD data and atleast one server comprising a tangible, non-transitory computer readablemedium having stored thereon an agent hoster subsystem and anotification subsystem. The agent hoster subsystem is configured tocommunicate with a plurality of Intelligent Agents, where eachIntelligent Agent is configured to perform a distinct dispatch-relatedanalysis of the CAD data and to produce dispatch-related notificationsbased on such analysis autonomously without being queried by the user.The notification subsystem is configured to determine, based at least inpart on feedback received from a user, whether to present a givennotification from a given Intelligent Agent to the user.

In various alternative embodiments, the Intelligent Agents may include astatistic agent configured to detect outliers in the CAD data and toproduce a notification when an outlier is detected. The statistic agentmay include a machine learning outlier detector trained to detectoutliers in the CAD data.

Additionally or alternatively, the Intelligent Agents may include akeyword agent that produces a notification when at least one specifiedkeyword is detected in an event.

Additionally or alternatively, the Intelligent Agents may include apattern agent configured to detect patterns in the CAD data and toproduce a notification when a pattern is detected in an event.

Additionally or alternatively, the Intelligent Agents may include anevent match agent configured to detect similar events based on a fuzzylogic analysis and to produce a notification when similar events aredetected based on the fuzzy logic analysis. The may include a machinelearning pattern detector trained to detect patterns in the CAD data.

Additionally or alternatively, the Intelligent Agents may include arepeated event agent configured to detect recurrent events and toproduce a notification when a recurrent event is detected.

Additionally or alternatively, the Intelligent Agents may include acorrelation agent that produces a notification when a combination ofvariables is determined to contribute to the occurrence of an event. Thecorrelation agent may include a machine learning correlation detectortrained to detect correlations in the CAD data.

Additionally or alternatively, the Intelligent Agents may include asimilarity agent configured to detect similar events based on wordfrequency and to produce a notification when similar events are detectedbased on word frequency.

Additionally or alternatively, the Intelligent Agents may include a ruleagent that produces a notification when operational variables match apreconfigured rule.

In any of the above embodiments, the notifications presented to the usermay provide a mechanism for the user to provide feedback regarding thenotification. The system may allow notifications to be shared betweenusers and/or with non-users. The system may attach a notification to arelated event. Intelligent agents may be implemented by the at least oneserver (e.g., on-site) and/or may be implemented remotely from the atleast one server (e.g., by a cloud-based machine learning service).

In any of the above embodiments, the agent hoster subsystem, one or moreIntelligent Agents, and/or the notification subsystem may be embodied asa computer program in a computer program product comprising a tangible,non-transitory computer readable medium.

Embodiments described and claimed herein have the effect of transformingCAD systems, which may be reactive or may require users to sift throughvast amount of data, into a virtual dispatch assist system withIntelligent Agents that can leverage artificial intelligence and machinelearning to proactively analyze CAD data and generate usefulnotifications to assist a dispatcher or automate dispatch operations.For at least these reasons, the activities described and claimed hereinprovide a technological solution (e.g., leveraging artificialintelligence and machine learning) to a problem that arises squarely inthe realm of technology (e.g., computer-aided dispatch systems) in amanner that is not well-understood, routine, or conventional to askilled artisan in the field of the present invention.

Additional embodiments may be disclosed and claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

Those skilled in the art should more fully appreciate advantages ofvarious embodiments of the invention from the following “Description ofIllustrative Embodiments,” discussed with reference to the drawingssummarized immediately below.

FIG. 1 is a simplified schematic diagram of a computer-aided dispatch(CAD) system, in accordance with certain exemplary embodiments.

FIG. 2 is a schematic diagram showing a functional model of a virtualdispatch assist system with Intelligent Agents, in accordance withcertain exemplary embodiments.

FIG. 3 is a schematic diagram showing a data model of the virtualdispatch assist system with Intelligent Agents of FIG. 2 , in accordancewith certain exemplary embodiments.

FIG. 4 shows one sample notification format.

FIG. 5 shows another sample notification format with more detailedinformation and an OnCall map.

FIG. 6 shows a shared notification in accordance with certain exemplaryembodiments.

FIG. 7 shows a graphical user interface for attaching a Notification toan event, in accordance with certain exemplary embodiments.

FIG. 8 shows a notification history listing in accordance with certainexemplary embodiments.

FIG. 9 is a schematic diagram showing a genetic algorithm workflow, inaccordance with certain exemplary embodiments.

FIG. 10 shows an example of a multiplication (cross-over) operation,where two most fit individuals (i.e., the parents) are divided into twosections (the cut point may be random) and the resulting sections arecombined such that each offspring includes one piece from each parent.

FIG. 11 shows an example of a mutation operation, where a portion of theelite population receives a mutation which can be random or rule-based.

FIG. 12 shows a fuzzifier function for determining if two events arenear to one another, in accordance with one exemplary embodiment.

FIG. 13 shows a fuzzifier function for determining if two eventshappened at the same time, in accordance with one exemplary embodiment.

FIG. 14 is a graphical view of the results of a correlation analysisshowing the average value of the events for all codes and the selected20% influence range for the case of TYCOD=4 (Aggression/Sexual Violence)correlated by Day of Week (DOW), in accordance with one exemplaryembodiment.

FIG. 15 is a graphical view of the results of a correlation analysisshowing the average value of the events for all codes and the selected20% influence range for the case of TYCOD=4 (Aggression/Sexual Violence)correlated by Hour of Day, in accordance with one exemplary embodiment.

FIG. 16 shows a fuzzy logic function for determining whether or not anotification is shown to the user, in accordance with one exemplaryembodiment.

FIG. 17 depicts the main workflow for the StatisticAgent in accordancewith an exemplary embodiment.

FIG. 18 shows an example baseline of variables.

FIG. 19 shows an example of an outlier notification with statistics.

FIG. 20 shows an example updated baseline for the above example.

FIG. 21 shows an example of related events shown in a map, including theability of the user to display event information such as to identify theoriginal event and related events.

FIG. 22 shows a more detailed example of similarity analysis inaccordance with an exemplary embodiment.

FIG. 23 shows the architecture of the EventClassificationAgent inaccordance with an exemplary embodiment.

FIG. 24 shows a detailed example of event classification.

FIG. 25 shows another example of event classification.

FIG. 26 shows the architecture of voice-to-text integration inaccordance with an exemplary embodiment.

FIG. 27 shows an example of how the ClusteringAnalysisAgent can helpidentify the best location for placement of tow trucks based on ahistory of towing events, in accordance with one exemplary embodiment.

FIG. 28 shows an example graphical user interface screen showing anoptimized route, in accordance with one exemplary embodiment.

FIG. 29 shows a specialized regression graph for a time estimationagent, in accordance with one exemplary embodiment.

FIG. 30 shows an example graphical user interface screen showing stagedlocations for various units, in accordance with one exemplaryembodiment.

FIG. 31 shows an example coverage criteria definition, in accordancewith one exemplary embodiment.

FIG. 32 shows an example location redundancy detail screen, inaccordance with one exemplary embodiment.

FIG. 33 shows an example of area coverage relocation, in accordance withone exemplary embodiment.

FIG. 34 shows a sample CAD system screen showing various types ofnotifications superimposed on a map.

FIG. 35 shows some sample notifications in greater detail.

FIG. 36 shows a sample similarity notification detail.

FIG. 37 shows a sample pattern notification detail.

FIG. 38 shows a sample keyword notification detail.

FIG. 39 is a schematic diagram showing the relationship between the CADdispatch server (referred to here as OnCall 10), the SmartAdvisorAgentHoster, and the CAD Dispatch Client Workstation, in accordance withone exemplary embodiment.

FIG. 40 shows a SmartAdvisor AgentHoster graphical user interfacescreen, in accordance with one exemplary embodiment.

FIGS. 41-51 show class models for the SmartAdvisor and variousIntelligent Agents, in accordance with one exemplary embodiment.

It should be noted that the foregoing figures and the elements depictedtherein are not necessarily drawn to consistent scale or to any scale.Unless the context otherwise suggests, like elements are indicated bylike numerals.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS Definitions

As used in this description and the accompanying claims, the followingterms shall have the meanings indicated, unless the context otherwiserequires.

Generally speaking, rule-based logic is a computer program in whichdecision logic is specifically programmed by a programmer to produce aparticular output based on particular types of inputs. For a simpleexample of rule-based logic, the CAD system might include a rule thatthe nearest available responder should respond to an event.

Generally speaking, machine learning (ML) is the application ofArtificial Intelligence (AI) to provide software systems the ability tolearn and improve themselves, from experience, without being explicitlyprogrammed. Instead, the programmer models the problem to be solvedrather than programming the solution itself, and the ML model is trainedto perform a given task based on sample data (referred to as “trainingdata”) in order to learn how to produce a particular type output basedon particular types of inputs. Thus, machine learning is useful forhard-to-solve problems that may have many viable solutions. ML solutionscan exhibit non-expected behavior due to the artificial intelligence andcan improve over time based on additional training data accumulated overtime. A computer program employing machine learning may be referred toherein as a Intelligent Agent.

Introduction

In this day and age, artificial intelligence (AI) and machine learning(ML) are being used ever more frequently across a wide range oftechnologies and applications. WIKIPEDIA™ provides a list of exemplaryapplications for machine learning, including agriculture, anatomy,adaptive websites, affective computing, banking, bioinformatics,brain-machine interfaces, cheminformatics, computer networks, computervision, credit-card fraud detection, data quality, DNA sequenceclassification, economics, financial market analysis, general gameplaying, handwriting recognition, information retrieval, insurance,internet fraud detection, linguistics, machine learning control, machineperception, machine translation, marketing, medical diagnosis, naturallanguage processing, natural language understanding, online advertising,optimization, recommender systems, robot locomotion, search engines,sentiment analysis, sequence mining, software engineering, speechrecognition, structural health monitoring, syntactic patternrecognition, telecommunication, theorem proving, time seriesforecasting, and user behavior analytics. It is recognized that MLprograms often fail to deliver expected results, e.g., due to lack ofsuitable data, data bias, and poorly chosen tasks and algorithms, amongother things.

The inventors of the present invention contemplate the use of AI and MLin Computer-Aided Dispatch (CAD) systems for providing real-time,embedded analytics that can be used by a dispatcher or by the CAD systemitself (e.g., autonomously) to make dispatch decisions. One significantdifficulty in applying AI and ML to CAD systems is in converting thetheoretical advantages of AI/ML into a concrete implementation thatprovides insightful and practical outputs for making dispatch decisionsand utilizes dispatcher feedback on an ongoing basis to improve learningand future outputs. Also, because dispatch decisions generally involve awide range of potential event types involving complex interrelationsbetween collected data types (e.g., data regarding events, eventoutcomes, personnel, vehicles, equipment, facilities, etc.), the AI/MLimplementation needs to be flexible to allow for incorporation of newtypes of data sources and new types of outputs that can be used fordispatch decisions. Thus, the present invention does not merely amountto “apply AI/ML to CAD systems” but instead provides specific ways inwhich AI/ML can be used in CAD systems to improve dispatch decisions.

FIG. 1 is a simplified schematic diagram of a computer-aided dispatch(CAD) system 100, in accordance with certain exemplary embodiments.Among other things, components of the CAD system 100 can be logicallydivided into a number of subsystems including a CAD server subsystem110, a dispatcher subsystem 120, and an optional personnel subsystem130. Embodiments of the exemplary CAD system 100 can (and typically do)have multiple instances of each subsystem. For example, an exemplary CADsystem 100 can include one personnel subsystem 130 for each emergencyresponder, multiple dispatcher subsystems 120 to allow for multipleemergency dispatchers to handle emergency calls and alerts within asingle dispatch center and/or across multiple dispatch centers, andmultiple CAD server subsystems 110 at one or more data centers toprovide for such things as high-availability service and load balancing.

The CAD server subsystem 110 includes a CAD dispatch server 111 thatinterfaces with one or more dispatcher subsystems 120 over a firstcommunication network, a mobile responder server 112 that interfaceswith one or more personnel subsystems 130 over a second communicationnetwork, a database system 113 in which various types of data aremaintained by the CAD dispatch server 111 and/or the mobile responderservice 112, and optionally a machine learning service 114 (which may bepart of the CAD dispatch server 111 or may be separate from the CADdispatch server 111) that utilizes machine learning to track data overtime. As discussed in U.S. patent application Ser. No. 15/384,874, whichwas incorporated by reference above, the optional machine learningservice 114 can be queried (e.g., by the CAD dispatch server 111) foranalytical data. The machine learning service 114 can be implementedon-site with the CAD dispatch server subsystems 110 or can beimplemented remotely, e.g., cloud-based.

The dispatcher subsystem 120 includes a CAD dispatch client workstation121 that the emergency dispatcher uses to manage dispatch-relatedinformation. For example, among other things, the dispatcher may use theCAD dispatch client workstation 121 to enter information regardingincidents and emergency responders, view the status of incidents andresponders, dispatch emergency responders, and interact with emergencyresponders such as by providing updated incident information.

As discussed in U.S. patent application Ser. No. 15/384,874, which wasincorporated by reference above, the optional personnel subsystem 130includes a monitor device 131 that is wearable by the emergencyresponder (e.g., a bracelet-type device) and a mobile responder clientdevice 132 (e.g., a smartphone-type device running aspecially-configured mobile responder client application). The wearablemonitor device 131 is in communication with (e.g., “paired” with) themobile responder client device 132 over a wireless communication system(e.g., Bluetooth or WiFi). The mobile responder client device 132 is incommunication with the CAD server subsystem 110 over the secondcommunication network, which may include a wireless communication systemsuch as cellular telephone/data network.

The mobile responder client device 132 is specially configured (e.g.,using special hardware and/or a purpose-built client application) totransfer various types of information between the wearable monitordevice 131 and the CAD server subsystem 110, although certain types ofinformation can be exchanged between the mobile responder client device132 and the CAD server subsystem 110 exclusive of the wearable monitordevice 131. Among other things, as discussed in greater detail below,the mobile responder client device 132 may transfer information from thewearable monitor device 131 and/or the mobile responder client device132 itself to the CAD server subsystem 110, e.g., to allow formonitoring the emergency responder, and also can receive informationfrom the CAD server subsystem 110 for presentation to the emergencyresponder on the mobile responder client device 132 and/or the wearablemonitor device 131 via transfer from the mobile responder client device132. The mobile responder client device 132 may provide a specialgraphical user interface through which the emergency responder can sendand/or receive various types of information.

In accordance with certain exemplary embodiments, the wearable monitordevice 131 includes one or more interface devices to collect statusinformation about the emergency responder (referred to herein forconvenience as “biometric” information). For example, the wearablemonitor device 131 may include one or more of a heart-rate sensor, askin temperature sensor, a galvanic skin response sensor, a blood oxygenlevel sensor, and/or other sensor for collecting biometric information.The wearable monitor device 131 transmits information derived from theinterface devices to the mobile responder client device 132, which maybe configured to process the information and/or transmit the informationto the CAD server subsystem 110 for processing.

The wearable monitor device 131 and/or the mobile responder clientdevice 132 may include other types of interface devices, such as, forexample, a microphone (e.g., to allow the emergency responder to speakto an emergency dispatcher or to monitor for gunshots or other sounds),a speaker (e.g., to allow an emergency dispatcher to speak to theemergency responder or to generate audible alerts to the emergencyresponder by the CAD server subsystem 110 or the emergency dispatcher),a camera (e.g., to allow the emergency responder to record pictures orvideos for evidentiary purposes and/or to send to the CAD serversubsystem 110 or emergency dispatcher), a tactile output device such asa vibrator device (e.g., to generate tactile alerts to the emergencyresponder by the CAD server subsystem 110 or the emergency dispatcher),a “unit emergency alarm” input (e.g., a button to allow the emergencyresponder to generate an alarm to the CAD server subsystem 110 oremergency dispatcher), a motion sensor such as an accelerometer orgyroscope (e.g., to monitor whether the emergency responder is moving orstationary), a position sensor (e.g., to monitor whether the emergencyresponder is upright or recumbent), a temperature sensor (e.g., tomonitor the environmental temperature in which the emergency responderis operating), and/or a location sensor such as a GPS sensor (e.g., toprovide location information to the emergency dispatcher), among others.Information derived from such interface devices may be processed by themobile responder client device 132 and/or sent to the CAD serversubsystem 110 for processing.

Thus, for example, the first responder may be monitored throughinformation obtained exclusively from the wearable monitor device 131 ormay be monitored through a combination of information obtained from thewearable monitor device 131 and information obtained from the mobileresponder client device 132.

The mobile responder client device 131 may send information to the CADserver subsystem 110 at regular intervals (e.g., via a Web Service API)or upon request from either the responder or the dispatcher. Each pairedwearable monitor device 132 and mobile responder client device 131 isuniquely identifiable to the CAD server subsystem 110 and security ispreferably implemented to prevent data breaches, e.g., using HTTPS-basedcommunications between the mobile responder client device 131 and theCAD server subsystem 110.

The CAD server subsystem 110 maintains various types of information inthe database system 113. For example, among other things, the CAD serversubsystem 110 maintains information on the various incident types thatmay occur, information on each incident that does occur, and informationon each emergency responder.

Exemplary embodiments of the present invention provide a virtualdispatch assist system (sometimes referred to herein as the“SmartAdvisor”) in which various types of Intelligent Agents (sometimesreferred to herein as “SmartAdvisor Agents”) are deployed, e.g., as partof a new CAD system architecture or as add-ons to existing CAD systems,to analyze vast amounts of historic operational data in the databasesystem 113 and provide various types of dispatch assist notificationsand recommendations that can be used by a dispatcher or by the CADsystem itself (e.g., autonomously) to make dispatch decisions. Theinventors envision that the virtual dispatch assist system and relatedIntelligent Agents employing machine learning, artificial intelligence,and real-time analytics will provide a marked improvement and key marketdifferentiator over more traditional CAD systems and will be aspringboard to more advanced features such as cloud-enabled features andreal-time (and even proactive/predictive) crime centers and “safecities” applications. In exemplary embodiments, the Intelligent Agentsrun autonomously in the CAD server subsystem 110 and/or in the optionalmachine learning service 114, with each Intelligent Agent configured toperform a distinct dispatch-related analysis of data in the CAD databaseand to produce dispatch-related notifications based on such analysisautonomously and proactively without being queried by the user. In anexemplary embodiment, the notifications are processed by an optionalmachine-learning filter on a per-user basis so that only notificationsthat are relevant to a particular user are presented to the user,although alternative embodiments may provide some or all of thenotifications from the Intelligent Agents to the users in an unfilteredmanner. The Intelligent Agents and optional notifications filtertherefore provide inputs to the dispatch decision-making process in amanner that the inventors believe has not been available in CAD systemsto date.

FIG. 2 is a schematic diagram showing a functional model of a virtualdispatch assist system with Intelligent Agents and optional notificationfilter, in accordance with certain exemplary embodiments. Among otherthings, the virtual dispatch assist system includes four mainsub-systems, namely a SmartAdvisor Input sub-system, a SmartAdvisorCommons sub-system, a SmartAdvisor Agents sub-system, and a SmartAdvisorNotifications sub-system.

The SmartAdvisor Input subsystem accepts inputs from one or more OnCallsystems (e.g., 911 and/or various call-in and online input systems), aDesktop system (e.g., from dispatchers), an Administrator system, and aTest Data system (e.g., data used for training the Intelligent Agents).

The SmartAdvisor Commons sub-system includes software and hardwarecomponents that allow Intelligent Agents to be used within the system,including a receiver (REST) API allowing the Intelligent Agents tooperate within the system and have access to CAD data and other tools.

The SmartAdvisor Agents sub-system includes an agent hosting module(“AgentHoster”) that communicates with various Intelligent Agents, witheach Intelligent Agent configured or trained to produce notificationsbased on a specific type of analysis of the historic operational data(e.g., one Intelligent Agent might detect patterns across events,another Intelligent Agent might detect similarities between events,etc.). The virtual dispatch assist system can support virtually any typeof Intelligent Agent and provides for Intelligent Agents to be added toand removed from the system in virtually any desired combination ofIntelligent Agents. Intelligent Agents can be implemented on-site withthe CAD dispatch server subsystems 110 and/or can be implementedremotely, e.g., by a cloud-based machine learning service. Thus, theAgentHoster may be configured to host on-site agents and interface withremote or cloud-based agents, e.g., passing events and other CAD data tothe agents and receiving notifications and other data back from theagents. FIG. 2 shows some possible types of Intelligent Agents that canbe included in the virtual dispatch assist system, including a StatisticAgent, a Rule Agent, a Pattern Agent, a Keyword Agent, an Event MatchAgent, a Correlation Agent, a Recurrent (Repeated Event) Agent, aLocation Agent, a Template Agent, and a Similarity Agent; these and someother possible types of Intelligent Agents are described in more detailbelow.

The SmartAdvisor Notifications sub-system includes modules for decidingwhich types of events to present to a dispatcher and for handlingfeedback from the dispatcher in response to notifications, e.g.,adjusting the types of future events that are presented to thedispatcher or providing the feedback for re-training one or moreIntelligent Agents in order to improve functionality of the IntelligentAgent. All of these subsystems have access to the database system 113(referred to here as the “SmartAdvisor Database” and depicted as beingaccessible to the four SmartAdvisor sub-systems) for accessing historicoperational data and storing new data. With reference to the componentsshown in FIG. 1 , the components of the virtual dispatch assist systemshown in FIG. 2 may be implemented as part of the CAD Dispatch Server111 or distributed between the CAD Dispatch Server 111 and the MachineLearning Service 114 (e.g., some of the Intelligent Agents could beimplemented in the Machine Learning Service 114). Thus, IntelligentAgents may be cloud-based and/or integrated/on-premises.

Generally speaking, each Intelligent Agent has special “skills” andoperates proactively to constantly analyze operational and other data toprovide notifications of specific environmental events. In an exemplaryembodiment, CAD data is transferred via the REST API to the AgentHoster,which stores the data for historical purposes and for the IntelligentAgents to have access to the CAD data. The AgentHoster providesnotifications from the Intelligent Agents to the SmartAdvisorNotifications subsystem for possible display on a dispatcher clientdevice. In turn, the dispatcher can provide feedback (e.g., theusefulness or value of a particular notification) from which the virtualdispatch assist system can learn the dispatcher's notificationpreferences and improve its own performance for generating futurenotifications. FIG. 39 is a schematic diagram showing the relationshipbetween the CAD dispatch server (referred to here as OnCall 10), theSmartAdvisor AgentHoster, and the CAD Dispatch Client Workstation, inaccordance with one exemplary embodiment. FIG. 40 shows a SmartAdvisorAgentHoster graphical user interface screen, in accordance with oneexemplary embodiment. Here, the user can view available agents, inputhistory, notifications, and other details of the operation of theIntelligentAgents.

FIG. 3 is a schematic diagram showing a data model of the virtualdispatch assist system with Intelligent Agents of FIG. 2 , in accordancewith certain exemplary embodiments. Here, the Intelligent Agents utilizelocal operational data as well as other data such as pre-definedcustomer preferences, behaviors, and goals to generate notifications andrecommendations which can be filtered through a set of rules (whichthemselves may be machine-learned) to determine which notifications andrecommendations ultimately are presented to the dispatcher.

The following is a sample event data structure used in certain exemplaryembodiments of the present invention:

Data Format/ Field Description Data Type Data sample EventID Uniqueevent identification Unique String UNICODE may be used to relate to Key“S2887502” more useful external information Description String ofunstructured text String UNICODE that describes the event. Sometimes thedescription is written to the field agents, but many times a simplifiedversion is written and most of information is done on audio. RemarksAdditional information that String RTF is associated with the event.(short text) This may be a non- structured short text with punctuationand more than one sentence Type Type code of the event, Integer or 21related to an event type table Unique Key SubType Subtype of the Type,usually Integer or a detail of the type Unique Key TimeStamps Date andTime of the Date time 20160501000519ES Creation of the event, where20160526213714ES we can derive: Time, Date, 20160527034230ES Hour, Dayof the Week. We may have different time stamps (start, arrival,departure, finished, etc.) EventDuration Time from two-time stamps. Timein hours Float Time to arrive (from 0.00740 departure to arrival), timeto execute (from arrival to finished), etc. Location Geographiccoordinates of Lat, Long or 34543862, the event, of the many UTMcoordinates 738733055 events in the process: creation, dispatch,arrival, leave, etc.. Address Given Address, that can be String geocodedto the Location UserID Some identification of the Text or caller (thismay be Unique String classified, or simple Key nonexistent in someservices) UnitID Identification of the Unique String dispatched Unit andits Key group DispatcherID Identification of the Unique Stringdispatcher and its group Key

The SmartAdvisor will communicate with its users by issuingNotifications (represented by the Notifier block in FIG. 2 ). In thiscontext, a Notification is the product or output of an intelligentanalysis that communicates relevant information to the user. Thefollowing is a proposed notification data structure format, inaccordance with certain exemplary embodiment:

ID (String) Sequential Number Type (String) Code related to the Originor type of the Notification Number (String) A quantified (INTEGER)figure related with the Notification Value (String) a value (percentage,distance, FLOAT) related to the Notification TextLong (String) ShortText of the Notification TextShort (String) Complimentary text, oraction related to the Notification Link (String) Action item or 2ndLayer of information Keywords (List<String>)—List of keywords related tothe Notification RelatedNotes (List<Notification>) (optional) EventID(List<Event>)—List of Events related to the Notification (optional)UnitID Unit or list of units related to the Notification (optional)

FIG. 4 shows one sample notification format.

FIG. 5 shows another sample notification format with more detailedinformation and an OnCall map.

In an exemplary embodiment, Notifications may include a user interfacethrough which the user can provide feedback regarding the value of thenotification, e.g., a LIKE/DISLIKE button or other type of rating input(e.g., high/medium/low or a rating from 1-10). The SmartAdvisorclassifies Notifications based on the user feedback (represented by theFeedback block in FIG. 2 ) to define, beforehand, whether or not theuser is willing to receive a particular type of Notification. In anexemplary embodiment, the technology used is a Bayesian Classifier thatuses characteristics of previous liked and disliked Notifications asguidelines for future ones. The system learns by tracking user feedbackprovided in previously presented Notifications. This filtering can beuser-specific, e.g., learning each user's preferences separately. Toassure some randomness to the Filter, a small percent of Notifications(e.g., defaulting to 5% but optionally configurable by theadministrator) are left unclassified such that users may receive a smallpercentage of unwanted/disliked Notifications. Related Notifications maybe filtered as a group, e.g., if an original event Notification is notshown to the user, then Notifications related to the original event alsomay not be shown to the user. Notification filtering is performed by theDelivererManager block in FIG. 2 .

In an exemplary embodiment, a user can create a shared notification,which in an exemplary embodiment is a portable link (URL) that can beexchanged among users to communicate relevant SmartAdvisor discoveriesand causes rendering of a webpage that duplicates the originalNotification information and optionally also other links andinformation. The system can restrict shared notifications to users ofthe system (and even to specific users) or can allow sharednotifications to be shared with others outside of the system. FIG. 6shows a shared notification in accordance with certain exemplaryembodiments.

In an exemplary embodiment, a user can attach a notification to anevent, e.g., as a special remark or comment, optionally along withadditional information such as a list of users who liked or disliked theNotification. Among other things, attaching a notification to an eventcan allow users to understand how the Notification was created and whythe information is relevant. The attached Notification can be used aspart of auditing or standard procedure reviews. FIG. 7 shows a graphicaluser interface for attaching a Notification to an event, in accordancewith certain exemplary embodiments.

In an exemplary embodiment, Notifications are stored in the SmartAdvisorDatabase along with user feedback, allowing for reviewing the history ofnotifications. FIG. 8 shows a notification history listing in accordancewith certain exemplary embodiments.

Machine Learning and CAD Applications

Machine-learning is a broad term and can be used in many different ways.Real-world applications usually integrate more than one type oflearning. There are many “ready to use” algorithm libraries withBayesian, Neural Network and many Clustering functionalities. All thesetechniques generally involve some problem modelling and data formatting.Although a lot of emphasis today is in Deep Learning, this is not theonly way and to create Machine-learning systems. Genetic Programming andRule-based programming also can be used in optimization and to triggerwarnings that come from other analysis/learning machines.

The following is a brief discussion of some machine learning styles andsome possible applications of these machine learning styles to CADsystems.

The Symbolist learning style is based on rules and deductive reasoning.Knowledge is represented by a set of rules, which can be represented bydecision trees and random forests, and the output is based on inferencesfrom these trees/rules. The rules may be updated using fuzzy logic, andthe trees can be trained. In terms of possible CAD applications, theSymbolist learning style involves storing a set of rules in the systemand therefore is relatively easy to implement, may consider existingdata or more elaborate data sets, and can generate powerful results forinferring on large data sets. The following are some examples ofpossible real-time CAD warnings based on the Symbolist learning style:

Unexpected increase in the number of events in the last two hours.

Number of critical events above normal level—Alert supervisor.

Hazardous event created within 200 meters of a Public School—Evacuationmay be required.

Fire reported closed to a sensitive site—Land security alert required.

The Bayesian learning style is based on statistics and probability.Knowledge is gathered in probabilistic terms, and the occurrence ofevents changes the chance of other events. Learning is based on thesestatistics. Applying statistics can unhide rules that lead toconclusions. Finding correlation among the data may lead to existingcausation that can be explored by the CAD system. In terms of possibleCAD applications, the Bayesian learning style can support rule-basedintelligence using probability, e.g., using Bayes theorem and Markovchain. The following are some examples of possible CAD applicationsbased on the Bayesian learning style:

There is a 95% chance of happening a new event in sector X in the nexthour—Dispatch a patrol unit?

There is a relevant increase in the occurrences of car accidents insector Z in the last 3 days—Dispatch a routine check?

Last month there was an uncommon increase in domestic violence events onSundays—Most events happened in sectors A, D and G.

The Analogizer learning style is based on group similarities, clusters,and patterns. Learning comes from finding similarities in the data.Algorithms look for events that are closer to each other and classifyevents by assigning similar characteristics to the group. It usesalgorithm like the nearest neighborhood, support vector, and clustering.The following are some examples of possible CAD applications based onthe Analogizer learning model:

Trying to classify an event by grouping similarities.

Grouping closer events may lead to better geographic decisions

Endemic events (related to a site) can be unveiled with this analysis.

Once a cluster of similar events is identified, the events can betreated the same way, or a previous successful response can be appliedto the events, and the CAD system can learn from the past success.

The Evolutionism learning style is based on Natural Evolution and theGenetic Algorithm. This is essentially nature's learning algorithm,i.e., the survival of the fittest. A population of possible solutions(DNA) are evaluated and the fittest survive to create the nextgeneration. It also can use techniques of “crossover” and “mutation” tocreate new alternatives. Generally speaking, there are only two basicrequirements: 1) being able to generate the “DNA” of a solution, and 2)being able to evaluate a solution. Using a lot of computer power, thegenetic and evolutionary algorithms can create unique and innovativesolutions. Genetic Algorithm can help improve other styles of learning(e.g., Neural Networks, Analogies, etc.).

The Genetic Algorithm evolves solutions using Natural Evolutiontechniques (Darwinian evolution). The solution is represented by a DNAlike structure. A Fitness function is associated with the solution (DNA)and it is used to evaluate adaptation. A population of possiblesolutions (DNAs) is randomly initialized and evaluated, and the fittestindividuals survive to create the next generation. Optimization can beunderstood as an evolution towards an optimum. The genetic algorithmscan create unique and innovative solutions. Since there is low controlof the solution, two separate runs may give two completely differentsolutions. Restrictions to the solution can be easily modeled aspenalties on the fitness function.

FIG. 9 is a schematic diagram showing a genetic algorithm workflow, inaccordance with certain exemplary embodiments. Each of a number ofsuccessive population generations, beginning with an initial populationgeneration, is analyzed to select and sort the fittest (elite) membersof the population according to predetermined criteria. These elitemembers represent the best adapted members of the current generation(and possibly the best adapted overall, if predetermined stop criteriaare reached). For each successive population generation, a newpopulation is generated by multiplication (cross-over) and/or mutationof the elite members of the previous generation.

This algorithm can be demonstrated by example using a DNA analogy. Eachpopulation generation includes a set of individual DNAs. The initialpopulation can be totally random. The population evolves over a numberof generations. The fitness function relates a chromosome (individual)to a value proportional to the quality of the solution. The functionwill define the adaptability of the solution, to select the mostadjusted. Population evaluation is usually the most complex andtime-consuming part of a genetic algorithm. It may utilize a heuristicevaluation and may accommodate local improvements (e.g., a mimeticalgorithm—Genetic Local Search). The elite population is defined as apercentage of the most adapted (e.g., 20%). The elite population thenreproduces/evolves to ensure that the best solution is moving towards aneven better solution. This reproduction/evolution involvesmultiplication (cross-over) and mutation. FIG. 10 shows an example of amultiplication (cross-over) operation, where two most fit individuals(i.e., the parents) are divided into two sections (the cut point may berandom) and the resulting sections are combined such that each offspringincludes one piece from each parent. FIG. 11 shows an example of amutation operation, where a portion of the elite population receives amutation which can be random or rule-based.

The Evolutionism learning model is especially useful for optimizationand multiple objection solutions. The following are some examples ofpossible CAD applications based on the Evolutionism learning model:

Vehicle routing with special restrictions.

Facility location and capacity for optimal response.

Re-location/Move Ups optimization.

Automatic Dispatch Planning.

The Connectionist learning style is based on Artificial Neural Networksand is generally considered the “state of the art” in machine learning,as exemplified by Deep Learning, a multi-level neural network. Learningis stored in a manner similar to brain “synapses.” The basic structureis the Artificial Neural Network. The Network is trained by applying aninput and a known output. This creates the “synapses” and, with time,the network is able to give the correct output to other inputs.Inputs/Outputs can be almost any type of data. The Connectionistlearning style can be very useful for classification (supervisedclassification) and generally needs a lot of training. The following aresome examples of possible CAD applications based on the Connectionistlearning model:

Image recognition.

Event classification (e.g., type, urgency, etc.).

Event detection.

Alarm classification.

Time and distance measurement (e.g., better ETA).

Technology Overview

The following is an overview of some AI/ML technologies that can beemployed by various Intelligent Agents in accordance with an exemplaryembodiment.

Rule-Based Logic

A rule, in the context of the SmartAdvisor, is a logical expression thatresults in a true or false statement. We could state that every rule hasan expression like this:

-   -   if (logical expression) then (issue a notification)

This technology usually encompasses a symbolic processing and a ruleevaluation engine, but in some cases the logical expression can besimplified to a list of variables and limits in a structured way, forexample:

-   -   if (variable.OPERATOR.limit) then (issue a notification)

Or in a more generic form:

-   -   if ((variable1.OPERATOR.limit1) AND        -   (variable2.OPERATOR.limit2) AND . . . )    -   then (issue a notification)

Here, we only consider the operator .AND. to join the list ofexpressions because the operator .OR. is the same of having two rules.

In an exemplary embodiment, the logical operators for the logicalexpressions are:

OPERATOR Description .EQ. Equal .GT. Greater Than .GE. Greater of Equal.LT. Less Than .LE. Less or Equal

The Variables are system variables that will be translated into a realnumber, and the limits are all real numbers or other system variables.To evaluate an expression, the system brings the values for the variableand the value for the limit and executes the operator, repeating thisfor all pairs of Variable and Limit.

Fuzzy Logic

While boolean logic deals with true and false usually associated with 1and 0, fuzzy logic is a variation of the logic where the values may beany number from 0 to 1. Fuzzy logic includes 0 and 1 as extreme cases oftruth, or facts, but also includes the various states of truth inbetween them, so it considers partial truth and is often used tofacilitate the expression of rules and facts like something that is“near to”, “far from”, “close to”, and “like the”, for instance.

The goal of the fuzzy logic is to relate a set of partial truths to afinal logical decision of true or false. It is a method that resembleshuman reasoning and thus is considered part of Artificial Intelligence(AI) applications: the representation of generalized human cognitiveabilities in software so that, faced with an unfamiliar task, the AIsystem can find a solution.

The inventor of fuzzy logic, Lotfi Zadeh, observed that humans, unlikecomputers, make decisions considering possibilities like “definitively”,“maybe yes”, “I don't know”, “possibly”, “absolutely” that arevariations of the yes (true) or no (false), but at the end, humanssometimes must decide into a “yes” or “no”. The typical algorithm com aFuzzy Logic Problem can be:

1. Define the input variables

2. Transform these variables into fuzzy sets with the Fuzzyfier

3. Construct a set of rules mapping the input to the output

4. Implement these rules in the FuzzIntelligence

5. Evaluate the inputs and generate the output

Fuzzy logic can be implemented in systems with various sizes andcapabilities in software and even in hardware. In an exemplaryembodiment, the fuzzy logic system is simplified into two modulesreferred to herein as The Fuzzyfier and the FuzzyIntelligence. TheFuzzyfier module transforms generic inputs (which may come in variousformats) into a set of fuzzy values (i.e., numbers from 0 to 1) that areinput to the FuzzyIntelligence module. The FuzzyIntelligence modulesimulates the human reasoning process by making fuzzy inferences on thefuzzy values based on IF-THEN rules to produce a logic output. A thirdcomponent could be used to “Defuzzyfy” and transform the logic outputinto an output action or value.

Another useful application is a Membership Function. This allowsquantification of a linguistic term and representation of a fuzzy setgraphically. A membership function for a fuzzy set A on the universe ofdiscourse X can be defined as μA: X→[0,1].

Among some of the advantages of the fuzzy logic include simplicity andthe possibility of translating a complex problem from many fields into asimple solution in human-like decision making. One disadvantage of usinga fuzzy logic is that there is no generic approach or automaticlearning; instead, every problem must be adapted to every situation.

As an example of matching events using a fuzzy logic system, let'sconsider creating a fuzzy logic engine that solves the problem:

-   -   Two events may be the same if they occur close to each other and        are reported at almost the same time. They may have the same        type, but sometimes users describe the events differently such        that they result in different types. Given two events (e1, e2),        are they the same?

Thus, given two events e1(x1, y1, t1, s1) and e2 (x2, y2, t2, s2) where:

x1, x2, y1, y2—reported UTM location of the events

t1, t2—Date-times of the events

s1, s2—types of the event 1 and 2

the Fuzzyfier module must translate these data into fuzzy inputs. Tomake the final decision, the system needs the following fuzzyinformation:

Are the two events near each other?

Did the events happened at the same time?

Are the two events of the same type?

In one example of determining if two events are near each other, let dbe the Euclidean distance from one event to the other. If d<d_(max) thenwe can say they are close to each other, where for instance d_(max)=100m. But if d=101 m are they still close to each other? And if they are105 m? To have a consistent solution, an exemplary embodiment can use afuzzifier function like the one shown in FIG. 12 , where:

${{Fuzzy}(d)} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu} d} \leq d_{\max}} \\{1 - {\left( {d - d_{\max}} \right)/\left( \frac{d_{\max}}{N} \right)}} & {{{if}\mspace{14mu} d_{\max}} < t \leq \left( \frac{\left( {N + 1} \right)d_{\max}}{N} \right)} \\0 & {{{if}\mspace{14mu} x} \leq \frac{\left( {N + 1} \right)d_{\max}}{N}}\end{matrix} \right.$

Here, we are using

$\frac{d_{\max}}{N}$as an arbitrary fraction of d_(max). With the help of this function, theevent will be considered “near” if Fuzzy(d)<0.5 (or other arbitrarynumber<1.0).

To determine if events happened at the same time, the same approach canbe applied to time, with the help of a similar generalized Fuzzifierfunction that can have different function variations, for example, likethe one shown in FIG. 13 , where:

${{Fuzzy}(x)} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu} x} \leq x_{\max}} \\{1 - {\left( {x - x_{\max}} \right)/\left( \frac{x_{\max}}{N} \right)}} & {{{if}\mspace{14mu} x_{\max}} < t \leq \left( \frac{\left( {N + 1} \right)x_{\max}}{N} \right)} \\0 & {{{if}\mspace{14mu} x} \leq \frac{\left( {N + 1} \right)x_{\max}}{N}}\end{matrix} \right.$

To determine if two events are of the same type, we can consider theexact type or a family of types, or even a scale of types. One exemplaryembodiment considers the exact types.

With these three types of fuzzy information (i.e., the events are near,the events happened at the same time, and the evens have the same type)we can now formulate a Fuzzy Intelligence function, for example, asrepresented by the following decision tree that can be translated intothese logical expressions:

-   -   IF (near and same_time) THEN return True (Same)    -   IF (not_near and not_same_event) THEN return False (not Same)    -   IF (not_near and same_time) THEN    -   IF(same_type) THEN return True (Same Event)        -   ELSE return False (not Same)

Other functions of the same type can be implemented with differentresults.

Basic Statistics

For the basic statistics of the events in a CAD operation, an exemplaryembodiment will be based on the assumption that the events aredistributed in a Normal (Gaussian) distribution. Thus, the concepts ofMean Value and Standard deviation can help to represent the data. TheMaximum and Minimum values are also monitored, as well four differenttime frames: hour (short time frame), day, day of week (DOW—a numberfrom 1-7), and month (long time frame). In an exemplary embodiment, thefollowing information is tracked for each event type:

HOUR Day DOW DataType DT HODnn (last hour) Dddmm 1-7 Month MmmyyyyCounting N DT_N_HOD01 DT_N_HOUR DT_N_D1503 DT_N_DOW3 DT_N_M032017Average AVR DT_AVR_HOD01 DT_AVR_HOUR DT_AVR_D0120 DT_AVR_DOW1DT_AVR_M012018 Std Deviat. STD DT_STD_HOD01 DT_STD_HOUR DT_STD_D1503DT_STD_DOW3 DT_STD_M012018 Maximum MAX DT_MAX_HOD01 DT_MAX_HOURDT_MAX_D0120 DT_MAX_DOW2 DT_MAX_M112018

The following is an overview of the statistical algorithm in accordancewith an exemplary embodiment:

Given a new event ei of type Ti

Process new event

-   -   Process Statistics for All Events—EVENT    -   Compute Counting, Average, Std Deviation And Maximum    -   Check Event Rules—Issue Notifications    -   Process Statistics for Type T events—TYPE[t]    -   Compute Counting, Average (AVR), Standard Deviation (STD),        Maximum    -   Check Type Rules—Issue Notifications    -   Save Event Status

where:

${AVR} = {{\frac{\sum e_{i}}{N}\mspace{50mu}{STD}} = \;\sqrt{\frac{\sum\left( {e_{i} - {AVR}} \right)^{2}}{N - 1}}}$

Now, let limit be the percentage that is allowed for a notification beissued, e.g., limit=1.25 (25% above the expected value) so, using theexample above, there will be a notification when:DT_N_HOUR>DT_AVR_HODBefore*limitDT_N_HOUR>DT_MAX_HODBefore (setting anew maximum for this hour)DT_N_DAY>DT_AVR_DOWCurrent*limitDT_N_DAY>DT_MAX_DOWCurrent (setting anew maximum for this DOW)Correlation Statistics

This is a simple algorithm for the detection of possible correlationsamong variables by identifying outliers in a simple statistical analysisof a set of uncorrelated events. The study applies this analysis to asample of the data and can be processed whenever a relative large amountof data is available.

Given a set of non-correlated events E={e₁, e₂, . . . e_(n)} where eachevent has a set of independent characteristics c_(ij), where:e_(i)=(c_(i1), c_(i2), . . . , c_(im)), that can assume some known setof finite values.

Each value for each characteristic are assumed independent of the valueof the other characteristic, although each characteristic can have itsown probabilistic distribution.

For example, let c₁ be the day of the week and c₂ be the type of theevent. It's fair to assume the types of event are distributed evenlyamong the days of the week, whatever is the type of the event. (i.e. theprobability of p(c₁) is equal to p(c₁/c₂)p(c ₁)˜p(c ₁ |c ₂ =C)

If a certain type of event is more common in a certain day or the week,there is a correlation among that type of event and that day of theweek. This trend can be traced if we compare the probability ofoccurrence of the event in that day with the probability of occurrenceof all events in that day. Indicating a possible correlation of thecharacteristic c₁ with the value c₂=C.p(c ₁ |c ₂ =C)≠p(c ₁)

This inference is a direct application of a Bayesian inference, where wecompute directly P(c₁), as the a-priori probability of the time toexecute, equals the probability of the day (as if there is no relation).And P(c₁|c₂=C) the a-posteri probability of the time to execute giventhe day. When they diverge, the data indicates a relation between thetwo variables.

In the example, let's say that the completion of an event in theweekends could be shorter than other days; we could say that there is apossible correlation of the time to complete an event with the weekends.

All the above relations need a level of significance in the data sample.Small data sets could be limited to identify these phenomena.

Algorithmically, given a set of events with associated variablesidentify the pair or variables that may be correlate. The correlation isobserved if the pair of variable data occurs more frequently than it isexpected.

The probability of an event variable to appear in an event is the ratioof the number of events where this variable appears to the totality ofpossible events. Thus, if a variable influences the occurrence of anevent, the probability conditioned to that variable will be greater thanthe probability without the presence of the variable. By comparing thesetwo probabilities we can identify possible influences:

Given two characteristics c₁, c₂ in an event data set E={e₁, e₂, . . .e_(n)}

where e_(i)=(c_(i1), c_(i2), . . . , c_(im)),

Repeat for every value C of c₂Let p(c ₂ =C) and p(c ₁)>α (where α=level of significance)If p(c ₁ |c ₂ =C)>β*p(c ₁) (where β=level of influence)

Then c₁ is influenced by c₂=C

The main evidence that there is an influence among the variables of anevent is when there is an unexpected behavior in the presence of a givenfactor. It is assumed that the occurrence of events follows a randompattern. When the hypothesis of randomness is not confirmed, aninfluence between the variables is identified. The software seeks toidentify repetitive behaviors that escape what can be considered asexpected. We want to identify the set of events that insist on behavingoutside the mean of the behavior of the other events, the so-called“outliers.”

For example, we can test the hypothesis that traffic accidents occurmore on weekends we can compare the likelihood of a traffic accidentoccur on a day of the week to the frequency of all types of accident tooccur on that day. If there is an influence of the day of the week, theprobability will be significantly higher on this day. Note that thisapproach is associated with the absolute number of events, that is,there must be a high number of events to the ratio have some statisticalsignificance. This technique is similar the technique known as MarketBasket Analysis which is a common technique in the analysis of purchasesin a supermarket in search of the association of the purchase ofproducts. Identifying unusual characteristics and consumption patterns.

This type of study allows analyzing the correlation of any 2 variablesrecorded for the events. But it cannot be confused with a causalrelationship between variables. This means that the fact that twovariables are correlated is not an evidence that one variable is thecause of the other. Common causes can influence both variables and causecorrelation.

To exemplify the application, we will analyze historical data of atypical emergency health care system. The system operates answeringemergency calls in the city. Each call is classified according topredefined complain list types (TYCOD). Let's look at the influence ofthe Day of the Week, Time of Day, and Location on the several types ofevents. In addition to the type of complaint and date are recorded: theplace of service with geographical precision, time of arrival, asub-classification of the type of event, the service unit, etc.

One exemplary study correlating between type of event (TYCOD) and daysof the week (DOW) with a factor of influence of 20% identified thefollowing influences:Influence of TYCOD=04[01.3%] in D.O.W=01 is higher than average [057.2%]Influence of TYCOD=04[01.3%] in D.O.W=07 is higher than average [02605%]Influence of TYCOD=23[01.4%] in D.O.W=01 is higher than average [035.6%]Influence of TYCOD=29[05.2%] in D.O.W=07 is higher than average [023.3%]Influence of TYCOD=30[02.2%] in D.O.W=01 is higher than average [025.9%]

where:

TYCOD Type Description 4 Sexual Assault/Violence 23 Overdose/Poisoning(Ingestion) 29 Traffic Accidents/Transportation 30 Traumatic Wounds

The result reports that there is above average growth on the number ofevents of aggression, overdose, traffic accidents and traumatic onweekends (DOW) (where 1=Sunday, 7=Saturday). A more in-depthinvestigation into these events may show, for instance, that they areassociated with a higher alcohol consumption on weekends.

This may be the common cause that causes more accidents on these days.In an exemplary embodiment, correlation analysis will indicate thatthere is a cause to be investigated, but not what it is. This analysisalso provides a graphical view for each of these codes. For example,FIG. 14 shows the average value of the events for all codes and theselected 20% influence range for the case of TYCOD=4 (Aggression/SexualViolence) correlated by Day of Week (DOW) and FIG. 15 shows the averagevalue of the events for all codes and the selected 20% influence rangefor the case of TYCOD=4 (Aggression/Sexual Violence) correlated by Hourof Day.

It can be observed that when DOW=1 and 7, there is a substantialincrease in the frequency of events, indicating the correlation.

Natural Language Processing (NLP)

Natural Language Processing refers to the use of communicating tocomputers using a natural language, in opposite to a structured commandlanguage. This is a field of Artificial Intelligence and involvesreading as well as writing. For this technology note, we will limit theapplication of reading a text and making decisions based on it, we willnot consider, in this study, the use of Speech, although this is part ofNLP.

The NLP is commonly divided into NLU—Natural Language Understanding—thatis making sense of a text input, and NLG—Natural LanguageGeneration—that is producing meaningful phrases and sentences. We willstudy only NLU.

Understanding a Natural Language is very hard, as the languages a richin form and structure. There are many layers of understanding:

Lexical—is a very primitive, in a word level, and it consider that theword belongs to the language (“bread” is an English word)

Semantic—is also associated with the language, and it considers themeaning of the word. (“bread” is a type of food made of flour andusually baked)

Syntax—considers the use of the word in the phrase. (Bread is deliciousmeans that the “bread” has a characteristic of being “delicious”)

Referential—Considers the referring words using pronouns (Bread isdelicious. It is made of flour—the it refers to bread)

The same phrase/word can have many interpretations and can lead to manymeanings, and analysis.

NLP has usually five steps: Lexical, Syntactic/Parsing, Semantic,Discourse and Pragmatic.

Lexical Analysis—It involves identifying and analyzing the structure ofwords. Lexicon of a language means the collection of words and phrasesin a language. Lexical analysis is dividing the whole chunk of txt intoparagraphs, sentences, and words.

Syntactic Analysis (Parsing)—It involves analysis of words in thesentence for grammar and arranging words in a manner that shows therelationship among the words. The sentence such as “The school goes toboy” is rejected by English syntactic analyzer.

Semantic Analysis—It draws the exact meaning or the dictionary meaningfrom the text. The text is checked for meaningfulness. It is done bymapping syntactic structures and objects in the task domain. Thesemantic analyzer disregards sentence such as “hot ice-cream”.

Discourse Integration—The meaning of any sentence depends upon themeaning of the sentence just before it. In addition, it also bringsabout the meaning of immediately succeeding sentence.

Pragmatic Analysis—During this, what was said is re-interpreted on whatit meant. It involves deriving those aspects of language which requirereal world knowledge.

Some AI applications can be accomplished at the level of lexical andother need a syntactic and a semantic understanding.

NLP Level Application Technology Lexical Analysis Text Pattern RegularExpression Extracting Identification Repeated Event IdentificationSyntactic Analysis Notification Bayesian Classification (Parsing)Likelihood Analysis Similarity Analysis Event Pattern AnalysisText Pattern Identification

One of the possibilities of finding patterns in a text is to define aregular expression pattern a highlight whenever the expression appearson a text. A regular expression in a pattern that consists of one ormore literals characters, operators, and other text structures. Mostprogramming languages have tools for extracting the text that matches aregular expression. The regular expression is described by a set ofconstructs, and when the input text matches these expressions we havethe desired pattern.

The following are some exemplary REGEX expressions for text patteridentification:

Item Expression City, state and zip #\d+ ([{circumflex over ( )},]+),([A-Z]{2}) (\d{5}) (\d*)\s+((?:[\w+\s*-])+)[\,]\s+([a-zA-Z]+)\s+([0-9a-zA-Z]+) Phone numbers @“(\(?[0-9]{3}\)?)?\-?[0-9]{3}\-?[0-9]{4}” License Plates and Registration [A-Z]{1, 3}-[A-Z]{1, 2}-[0-9]{1,4} {circumflex over ( )}[A-Z]{1, 3}-[A-Z]{1, 2}-[0-9]{1, 4}$ SocialSecurity Number {circumflex over( )}((?!000)(?!666)(?:[0-6]\d{2}|7[0-2][0-9]|73[0-3]|7[5-6][0-9]|77[0-2]))-((?!00)\ d{2})-((?!0000)\d{4})$Keywords Identification

One of the possibilities of finding patterns in a text is to define aset of pre-defined keywords, and when some of these keywords are foundin the event description, the system generates an appropriatenotification, e.g., to a specific user who requires this information.

Notification Likelihood Analysis

The user feedback (e.g., thumbs up or thumbs down) will teach theVirtual Assistant whether or not similar Notifications should betransmitted to the user. In other words, the system can estimate thelikelihood probability of a notification, e.g., if the user, based onprevious information, will like the notification or not. One exemplaryway of doing that is by a Bayesian classifier, as described below.

The result of the classifier is to give an estimation of the probabilityof the likelihood (of a notification, given the previous information ofthe feedback from this user for the notifications he had received untilnow. We can also estimate the probability of the dislike of theinformation.

Let PL(Ni) be the Probability of Likelihood, where the closer to 1.0means the user will probably like the notification. Let PD(Ni) be theprobability of the user dislike the notification, based on previousdislikes. With PL(Ni) and PD(Ni) we can use fuzzy logic, and create aFuzzy Intelligence logic to decide whether or not the Notification isshown to the user, for example, as shown in FIG. 16 .

An exemplary embodiment also includes some serendipity, e.g., byproviding some small random number of notifications to the user,regardless of the classification. This gives the user the chance toreview his or her preferences.

The Bayesian Classifier

One technique used to automatically classify notifications is theBayesian Classifier. It is based on the statistical theorem of Bayes,that estimates the conditional probability of an event, given theprevious knowledge that another related event has occurred. In thiscase, we can estimate the probability that the notification will beliked by the user, that is the Notification is of class C given that aset of parameters T (notification description) was used to describe it.In other words, we will estimate the likelihood of the user liking thenotification based on previous likes given by the user in other notes.

This technique is like the one used to classify spam in emails or toclassify important and promotional emails in our inbox. It looks for thefrequency of certain words and makes a prediction, that can be correctedby the user with new feedback.

Let the Bayes Theorem be expressed by:

${P\left( {C❘T} \right)} = \frac{P\left( {T\left. C \right)*{P(C)}} \right.}{P(T)}$

where:

P(C|T) is the conditional probability of a class given a set ofparameters T, the wanted result;

P(T|C) is the conditional probability of a given parameter in the classC (estimated by the history of previous occurrences);

P(C) is the probability of the Class, also estimated from previousevents;

P(T) is the probability of the occurrence of a given parameter, alsoestimated from previous events, as the parameter set is composed of anindividual item:T={W ₁ ,W ₂ . . . W _(n)}.

For a Notification, we can have, for instance: Notification Type,Notification Location, Notification Text (set of words), etc.

The P(T|C) can be expressed as:P(T|C)=P(W ₁ |C)*P(W ₂ |C)* . . . *P(W _(n) |C)andP(C|T)=P(W ₁ |C)*P(W ₂ |C)* . . . *P(W _(n) |C)*P(C)

An implementation may be structured into two phases: Training andtesting. In the training phase, parse, eliminate the rejected words thephrases, eliminate the rejected words, and count the remaining ones foreach class. In the testing phase, receive a text input, parse the input,and calculate the probability of the input belonging to each class,selecting for the most probable one.

Probabilities are estimated by the counting of the words, as:

${{P(C)} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{{T_{i}(C)}\mspace{31mu}{and}}}}}\;$$P\left( {{W_{n}\left. C \right)} = \frac{\sum{T\left( {W_{i}❘C} \right)}}{\sum\mspace{14mu}{T_{i}C}}} \right.$Event Pattern Analysis

The feature of Event Pattern Analysis goal is to highlight events thatshare a similar pattern. There are many ways to define a pattern in thiscontext, but one exemplary embodiment will consider two differentapproaches: Repeated Event Identification and Event Similarity.

Repeated Event Identification

This feature looks for repeated events in the historic data base thathave the same place and type. It is goal is to find repeated offendersrather simple crimes that may escalate in their consequences, likedomestic violence, etc.

For this Notification, we can use the same technique of the event matchwith a fuzzy-logic comparison of the events, with a very tight tolerancein the distance and the same type, and do not regard the date in theanalysis.

Event Similarity

This is the technology involved in assessing short texts and evaluatingtheir distance, or in other words, their similarity. Two texts may beconsidered similar if they are referred to the same event. Thisdefinition can consider simply the syntax of the text (word and termsused in the text) or the semantics (meaning) of the words and the text.This will be explored by NLP—Natural Language Processing techniques tofind patterns while comparing events and social posts. One exemplaryembodiment defines a cosine text similarity as a value between 0 (nosimilarity) and 1 (very similar, or equivalent).

Distance also can be considered when dealing with the fuzzy logiccomparisons, like near, far, close to, sooner, later than, etc. Forexample, given two sets of features:x _(i)=(x _(i1) ,x _(i2) . . . x _(id)) and x _(j)=(x _(j1) ,x _(j2) , .. . x _(jd))

where d is the dimensionality of the feature set, the relationshipbetween these two sets can be evaluated as a distance. A distance can beused to measure continuous features, while qualitative variables can usethe concept of similarity.

A generic distance formula is known as Minkowski distance, and isexpressed by:

$D_{ij} = \left( {\sum\limits_{l = 1}^{d}{{x_{il} - x_{jl}}}^{n}} \right)^{1/n}$

If n=1 the formula represents the Manhattan or “city block” distance,while if n=2 the formula represents the Euclidean distance, as shown inthe table below (fromhttp://www.molmine.com/magma/analysis/distance.htm):

Euclidean d(x,y) = {square root over (Σ(x_(i) − y_(i))²)} SquaredEuclidean d(x,y) = Σ(x_(i) − y_(i))² Manhattan d(x,y) = Σ|(x_(i) −y_(i))| Canberra${d\left( {x,y} \right)} = {\sum\frac{{x_{i} - y_{i}}}{{x_{i} + y_{i}}}}$Chebychev d(x,y) = max(|x_(i) − y_(i)|) Bray Curtis${d\left( {x,y} \right)} = \frac{\sum{{x_{i} - y_{i}}}}{{\sum x_{i}} + y_{i}}$Cosine Correlation${d\left( {x,y} \right)} = \frac{\sum\left( {x_{i}y_{i}} \right)}{\sqrt{\sum{\left( x_{i} \right)^{2}{\sum\left( y_{i} \right)^{2}}}}}$Pearson Correlation${d\left( {x,y} \right)} = \frac{\sum{\left( {x_{i} - \overset{\_}{x}} \right)\left( {y_{i} - \overset{\_}{y}} \right)}}{\sqrt{\sum\left( {y_{i} - \overset{\_}{y}} \right)^{2}}\sqrt{\sum\left( {y_{i} - \overset{\_}{y}} \right)^{2}}}$Uncentered Pearson Correlation${d\left( {x,y} \right)} = \frac{\sum{x_{i}y_{i}}}{\sqrt{\sum\left( {y_{i} - \overset{\_}{y}} \right)^{2}}\sqrt{\sum\left( {y_{i} - \overset{\_}{y}} \right)^{2}}}$Euclidean Same as Euclidean, but only the indexes where both x and yhave a Nullweighted value (not NULL) are used, and the result isweighted by the number of values calculated. Nulls must be replaced bythe missing value calculator (in dataloader).Cosine Text Similarity

To define one short text description (d1) to another short description(d2) we can use some analogies to vector distance. A popular measure isthe Euclidean distance, defined by the root of the square differencesbetween the two vectors:Euclidean(d ₁ ,d ₂)=√{square root over ((d ₂ −d ₁)·(d ₂ −d ₁))}Where A·B considers the inner product of the two vectors, giving themeasure a unique real value. To compare two measures, we need to definea scale. Another very used measure among vectors that has its own scaleis the cosine of the angle of two vectors, that may be expressed by:

${{Cos}\left( {d_{1},d_{2}} \right)} = \frac{d_{1} \cdot d_{2}}{\sqrt{d_{2} \cdot d_{12}}\sqrt{d_{1} \cdot d_{1}}}$

Two vectors are close to each other if the cos is close to 1 and theyare apart if cos is zero, this gives a nice approach to a percentage ofsimilarity. Considering each short text description as a sequence ofwords:d={w ₁ ,w ₂ , . . . ,w _(N)}thend ₁ ={w ₁₁ ,w ₁₂ , . . . ,w _(1N)}d ₂ ={w ₂₁ ,w ₂₂ , . . . ,w _(2N)}andd ₁ ·d ₂ =d ₁ ·d ₂ =+w ₁₂ w ₂₂ + . . . +w _(1N) w _(2N)or

${d_{1} \cdot d_{2}} = {\sum\limits_{i = 1}^{N}\left( {w_{1\; i}w_{2i}} \right)}$

As this equation shows, the more words they share, the closer they are.But not words can be considered equally important in the descriptions.In a police description, for example, the characteristics of the suspectis more important than the fact that was an armed robbery or the list ofstolen items. To consider this difference among the words, one exemplaryembodiment uses the TF-IDF algorithm as a weight (f_(i)) in thissummation, as follows:

${d_{1} \cdot d_{2}} = {\sum\limits_{i = 1}^{N}\left( {f_{i}w_{1\; i}w_{2i}} \right)}$TF-IDF Algorithm

TF-IDF stands for term frequency-inverted document frequency. This indextends to filter uncommon terms from a short text:tfidf(w,d,D)=tf(w,d)·idf(w,D)

where:

tf(w, d)—indicates the frequency of the word w in the document; and

idf(w, D)—measures the inverse term (w) frequency in the wholecollection of documents (D).

The value of tf(w, d) is zero (if the word does not appear in thedocument) or greater than 1, and we can assume 1 and consider every wordindividually. The use of a square root and a logarithmic function helpsto increase the rate among the distribution and promote the lessfrequent terms:

${{idf}\left( {w,D} \right)} = {\log\left( {1.0 + \left( \frac{ND}{Nw} \right)} \right.}$Exemplary Intelligent Agents

The inventors contemplate various types of Intelligent Agents that canbe incorporated in the system, likely with different Intelligent Agentsand different Intelligent Agent types deployed for different customers.The agent hoster generally allows Intelligent Agents to be added,removed, and replaced as needed or desired. Without limitation, thefollowing are some exemplary Intelligent Agents contemplated by theinventors.

A StatisticAgent can be included to monitor the operational variables ofthe system and notify of outliers. An outlier is a variable which valueexceeds an expected limit. In an exemplary embodiment, the expectedvalue is determined by measuring the variable during a training periodwhen the allowed limit is predefined. The main impetus for monitoringoperational variables is that normal operation should be expected toremain substantially stable, i.e., the expected values of the monitoredvariables should remain within acceptable limits, so when a variablecrosses this limit, some abnormality may be happening in the operationand thus it must be alarmed. The more stable the operation, the moreimportant deviation alarms are, as they will probably reflect anon-expected occurrence in the operation. If the operation is veryvolatile, i.e. there is not much recurrence of events, the deviationalarms have a minor relevance, as there it is natural for this operationto change the number of occurrences without an external reason.

For purposes of the following discussion, the following terms aredefined:

Categorical variables are operational variables that are expresses interms of a category. For example, the event type is a categoricalvariable. The monitoring of a categorical variable is done by countingthe variable during a period and observing the frequency of thiscategory among all categories.

Continuous variables are operational variable expressed by a real value.For example, the Time to Arrive at an event in minutes. The monitoringof a continuous variable can also be done by estimating the averagevalue during a period.

Outlier is a measured of a monitored variable that is beyond someexpected limit. The expected value is determined by measuring thevariable during a training period, and the allowed limit if predefined.

Training is the period during which the variable is monitored to definethe expected values for each category (if it is categorical), or theaverage value (if it is continuous).

Checking-Period, after the training period, that the variable isverified for outliers. This period can be repeated indefinitely.

Threshold is the limit value, expressed as a percentage deviation, thatis a variable is considered an outlier, e.g., a threshold of 50% meansthat if the frequency during checking exceeds 50% of the expected value,we have an outlier.

Support is the minimum frequency of a variable that is consideredstatistical relevant. If a variable frequency is below the supportvalue, the outlier is not discharged. The use of a support value is toavoid estimating statistical from very infrequent events.

In an exemplary embodiment, variables are classified into two types:categorical variables (that has the value defined in categories, likethe event type), and continuous variables (that the values range from aminimum to a maximum continuously, like the time to arrive at theevent). Each variable will be managed differently. The categoricalstatistic will be measured by the number of events per unit of time,e.g., measured in hours. It should be noted that continuous variablescan be monitored as categorical, e.g., by organizing them intocategories.

In an exemplary embodiment, each variable is defined by the followingdata structure:

Parameter Description Typical Value Name Name of the available variablesTYP Description Description of the Monitored Hourly Event Type VariableDTS Start Initial date and time stamp 1^(st) Event DTS for monitoringLearning Period Initial period for creating Week (168) a baselineChecking Period Repeated Checking period Hour (24) Threshold %difference that configures 50% (0.5) an outlier Support Frequency wherewe consider 2% (0.02) relevant to monitor Continuously If the Agentshould learn Yes Leaning after every checking period or keep the initialbaseline

The Periods can be standard as Hour, Shift, Day, Week, Month and anyother time expressed in time, as follows:

Period Hours Hour 1 Shift 8 Day 24 Week 168 Month 720 (aprox.)

The following are Monitored Variables of the CAD Operation in anexemplary embodiment:

Variable Typical Checking Var. Code Type Variable Description PeriodTYPE Categorical Event Type Day STY Categorical Event Subtype HODCategorical Hour of the Day Day DWO Categorical Day of the Week Week DRGCategorical Dispatch Group Day ETA Continuous Time to Arrive at theevent Less than a Day

In an exemplary embodiment, the StatisticAgent has three major classes,as follows:

StatisticAgent—main class that stores a list of Categorical Variablesfor monitoring and receives events continuously, parses the monitoredvariables, and passes them to Monitored variables. When the variablesidentify outliers, they issue notifications that are returned to theAgent.

Categorical Variable—its main concern is to monitor a variable. Itidentifies automatically each category and monitor its occurrences.During an initial learning phase, the input is used to train a modelthat defines a baseline that is the expected value, that will be checkedagainst the monitored variable that is checked periodically.

CategoricalVariableStats—this class holds the list of categories and thefrequencies.

In an exemplary embodiment, the agent is set up by adding a list ofmonitored categorial variables, suggested as the table of monitoredcategorical variables of CAD operation. After this setup, the agentfollows continuously the main workflow for every event if receives. FIG.17 depicts the main workflow for the StatisticAgent in accordance withan exemplary embodiment. First, the external Caller inputs sequentiallya series of events and gets as a return a list of Notifications(Method—Trigger). Then, the Agent extracts from the Event the featuresfor each categorical Variable and sends the feature data to thecorresponding CategorialVariable that monitors it, requesting from thema list of possible outliers (Method—getOutliersNote). During thetraining phase, the Monitor stores the feature and calculates thefrequency for each variable (Method—Monitor.Add). At the end of thetraining phase, the Monitor is cloned to a Baseline(Method—Monitor.Close). The Baseline is sorted and the frequency iscalculated (Baseline. SorteByCategory, Baseline.setFrequency). Duringeach Checking cycle, the feature is stored in the Monitor(Method—Monitor.Add). At the end of a Checking cycle, the Monitor iscompared with the Baseline with via a Check method and a set of outlierscan be issued (Method—Check). The Outliers are transformed intoNotifications at the end and the Notifications are returned to theCaller, as an output (Method—GetNotes).

FIG. 18 shows an example baseline of variables.

FIG. 19 shows an example of an outlier notification with statistics.

In this example, there was a 68% increase of MISCELLANEOUS events(expected 24 events and was registered 40 events), and a 60.8% increasein GARBAGE MISSES (expected 7 and measured 12) in this period (24hours). There also was a 71.3% decrease in SIGNAGE REPAIR in the sameperiod. An updated Baseline incorporating this week's data is generated,as the algorithm is continuously learning. FIG. 20 shows an exampleupdated baseline for the above example.

The following are some examples of main statistical notifications:

Example Additional Information 10 Outliers found in the Hour = 16h00Each outlier notification 4 Outliers found last Week of 20 Mar. 2019Each outlier notification 3 Outliers found in the Month of March/2019.Each outlier notification

The following are some examples of outlier notifications:

Example Additional Information 45% Car accident event type exceed Listof related events in the map exceeds the expected frequency StatisticGraph observed in the last day. 60% increase In Domestic violence- Listof related events in the map event type in the last month StatisticGraph

A Statistic Graph may be presented to the user. A Statistic Graphdisplays, in graph form, the outliers of a statistical analysis. In anexemplary embodiment, the graph shows the expected value and the actualvalue and identifies why this is an outlier. baseline graph may beavailable to show the expected values in a separate Notification, oncethe learning phase is done. If the number of items is very large, alimited number of items can be displayed, e.g., the top 20% or bylimiting the number of bars.

A RuleAgent can be included to monitor some user-defined rules involvingoperational variables and issue notifications when the rules areverified. In an exemplary embodiment, the list of available operationalvariables is pre-defined and may include the statistical monitoredvariables (see StatisticAgent above). The limits and thresholds can befixed values or can be combinations with system variables. Rules as wellas rule-related notifications are defined by the user. In an exemplaryembodiment, a rule is generally defined as follows:

if (logical expression) then issue a notification

The following is an example rule and notification configuration:

If ((Number of Robberies in the last Hour)>5)

Then (“More than five robberies in the last Hour, warn the chief ofPolice”)

The following are some examples of rule warning notifications:

Example Additional Information 5 rules checked in the last events RuleIndividual Notification 2 rules checked in the last hour Rule IndividualNotification

The following are some examples of Rule Individual Notifications:

Example   3 Armed-robberies have occurred in the last 4 hours with aproximity to each other 50 ft is the distance of this event to a publicschool. Consider evacuation procedure.

A PatternAgent can be included to identify a pre-defined patternexpression in the event description (e.g., type, description, remarks,comments, location) and to issue a Notification to the users with anassociated action along with it. A pattern expression describes adefined configuration of numbers, letters, and/or symbols, e.g., aregular to expression. The action can incorporate the identified patternin it, e.g., a URL/Link or an API call that relates to the externalaction. The PatternAgent works on a list of Named Patterns with theassociated regular expression and the action associated with it. In anexemplary embodiment, a set of pre-defined expressions is provided, butusers are free to create their own, using the Regular Expression syntax.There is a regular expression that represents phone numbers, emails,hashtags, plate numbers, social security numbers, etc.

The following are some examples of Pattern Warning Notifications:

Example Additional Information 5 patterns found in the event C2344-Individual Pattern Notification AS 2 patterns found in the event 2340-Individual Pattern Notification ABC

The following are some examples of Individual Pattern Notifications:

Example Additional Information License plate id [NNN-NNNNN] Additionalinformation identified in event descriptions. can include a vehicleCheck it with DMV. database information report Phone number [XXX-XXXXXX]Additional information can identified in the Event Description. includean option for the user to call the phone number

A KeywordAgent can be included to detect keywords in the eventdescription and alerts users on actions related to the keywords. In anexemplary embodiment, the keyword must be detected literally. Every wordon the keyword must be present and no typos are allowed. The words mayappear in a different order and they still are considered. The words arenot case-sensitive. Partial words are also considered, so, for example,the keyword “Manifest” would match the word “Manifestation” in the eventdescription by the keyword “Manifestation” would not match the word“Manifest” in the event description. Typos and synonyms are notconsidered at this phase, although alternative embodiments couldconsider typos and synonyms, e.g., using a simple word proximity measureto catch typos. Synonyms could be used with natural language processing(NLP) tools already available.

The following is an example of matching and non-matching keywords:

Key Word: Main Square Manifestation

Action: Warn the mayor (phone 2034-0293)

Matches: Manifestation on the Main Square

-   -   Main Manifestation Square    -   There is no manifestation on the main square

No Match: Huge demonstration on the main square

-   -   Central square manifestation    -   2000 people are manifesting on the main square

The following are some examples of keyword warning notifications:

Example Additional Information 5 keywords found in the event XYS-002Individual Keyword Notification 3 keywords found in the event ABC-Individual Keyword Notification 2340 1 keyword found in the event C32450Individual Keyword Notification The following keywords [keyword list]were recently reported in the event [EventID]. Action required: Pleaseinform Detective Joe (435-09029)

The following is an example of an Individual Keyword Notification:

The following keywords [keyword list] were recently reported in theevent [EventID]. Action required: Please inform Detective Joe(435-09029)

An EventMatchAgent can be included to identify similar events among thecurrent open events even if the description (e.g., event type, location,date) is slightly different. In an exemplary embodiment, the comparisonuses a fuzzy logic approach that can accommodate some variations andtherefore identify some minor variations. Fuzzy logic considers termslike “close” and “near” into the comparison. It translates these “fuzzy”terms into a logic of comparison. In an exemplary embodiment, a fuzzylogic tolerance parameter [Fuzzy] is defined, e.g., 25% by default.

The following is an example of a Matching Events Warning Notification:

Example Additional information 3 Events share the same Related events onthe characteristics with event C234509 Map

FIG. 21 shows an example of related events shown in a map, including theability of the user to display event information such as to identify theoriginal event and related events.

A CorrelationAgent can be included to analyze a pair of variables andtheir possible correlation to the occurrence of events. A notificationis issued when there is some probability that the combination of thesevariables may be contributing to the occurrence of the event (note thatthis is a correlation, not necessarily causation). In an exemplaryembodiment, the unit for estimating the statistic is the number ofevents per unit of time measured in hours, and the variables aremeasured by counting the events, so every new event is counted andmeasured to define the expected values and the outliers. In an exemplaryembodiment, the technique used is called “a priori” analysis and thefollowing are defined for the CorrelationAgent: List of Variable1,Variable2, Checking Period, Threshold, and Support. Variable1 andVariable2 are operational Variables to be analyzed. Checking is thePeriod, after the training period, that the variable is verified foroutliers. Threshold is the limit probability that is considered anoutlier, e.g., 50% means that if the frequency during checking exceeds50% of the expected value, there is an outlier. Support is the minimumfrequency that is considered relevant. If a variable frequency is belowthe support value, the outlier is not measured. Lift is the percent ofincrease on the expected value. The lift must be higher than theThreshold for the correlation to be considered.

The following are some examples of Correlational Warning Notifications:

Example Additional Information 10 Correlations found in the last week.Individual Correlation Notification 4 correlations found in the lastshift Individual Correlation Notification

The following are some examples of Individual Correlation Notifications:

40% more Drug Related events Type Map of the events that are associatedwith Sunday correlated. reported in last week Graphic of thedistribution of Correlation 110% more Overdose event Type Map of theevents that are associated with the correlated. Downtown Region inGraphic of the distribution of the last Month Correlation

A RepeatedEventAgent (sometimes referred to as a RecurrentAgent) can beincluded to identify events that have happened within a specified timespan [TSPAN, e.g., TSPAN=180 for 6 months or TSPAN=365 for 1 year] inthe past (perhaps repeatedly) and to warn the dispatcher that this maybe a recurrent event. Sometimes, a simple event (e.g., a domesticdisturbance) can scale to a larger event due to its recurrence, and insuch cases it sometimes is important to warn field personnel. Sometimes,dispatchers like to assign units that may have had previous knowledge ofan incident. Reminding that an event had happened in the past may beuseful for assigning a unit to respond. This agent needs access to pastevents that may be stored in the CAD system. In an exemplary embodiment,the search is done by location and event type. In an exemplaryembodiment, the RepeatedEventAgent considers the exact location (e.g.,same home address) as opposed to approximated ones, although alternativeembodiments could broaden the search (e.g., same neighborhood).

The following is an example Repeated Events Warning Notification:

Example Additional Information The event 23A- ESR has List of events andevent happened 4 times in the past data year

A SimilarityAgent can be included to find events that are similar to thecurrent event (e.g., a new event entered into the system) based on theirdescription (e.g., type, description, remarks, comments, location,etc.). Two similar events share relevant information, although they maynot have the same type, date or location. In an exemplary embodiment,this similarity index takes into account the words used in thedescription and remarks of the event. In an exemplary embodiment, thealgorithm values less frequently used words so when they appear indifferent events, the similarity index grows. The agent learns byanalyzing the frequency of the words used in a corpus of events, and itis constantly learning and updating the list of words and creating adictionary model. In an exemplary embodiment, no typo correction orsynonyms are considered in this phase, although alternative embodimentscould consider typos and synonyms. Clusters can help find patterns ingeographic information and in other forms of data. Embodiments may findpatterns in the geographical distribution and include other informationin the analysis. Clustering techniques can help find “clusters” ofsimilar events. A text/keyword analysis is used to infer potentiallyunobvious relationships between events. Through a dashboard/monitor (orOnCall notifications), the system can display potential linked eventsand other information that may be relevant to the user. TheSimilarityAgent could be extended for a Major Event Predictionmodule/workflow when a pattern becomes frequent.

In an exemplary embodiment, the following are defined for theSimilarityAgent: Similarity Threshold, Rejected Word List, TokenizerList, and Rejected Numbers. Similarity Threshold [similarity] is anindex from 0 to 1 above which the two descriptions are consideredsimilar, e.g., default [similarity=0.8]=80%). Rejected Word List (File)is a list of words that are not considered in the analysis, usuallywords that are very common and without a relevant information. TokenizerList is a list of symbols that are used as word separators. RejectNumbers (yes/no) defines whether or not to reject all numbers in thedescription (default=yes).

In an exemplary embodiment, the SimilarityAgent is based on acombination of tf-idf (e.g.,https://en.wikipedia.org/wiki/Tf%E2%80%93idf) and a cosine similarity ofthe two events (e.g., https://en.wikipedia.org/wiki/Cosine_similarity),where the frequency of words in the event description are analyzed,less-frequently used words are considered to be more important, and thencosine similarity is used to determine the ultimate similarity score. Inan exemplary embodiment, users may tag specific keywords, e.g.,locations of interest, people, places, etc. If these keywords appear, anotification is issued. Embodiments also can look for similarities intweets and other social media text messages. Search criteria (e.g., ahashtag or location) can be used to select such social media textmessages for analysis.

The following are some examples of Similarities Warning Notifications:

Example Additional Information 5 events are similar to the event[EventID] Individual Similarity Notification 1 events are similar to theevent [EventID] Individual Similarity

The following is an example Individual Similarity Notification:

Example Additional Information 80% of similarity between these Similarevents and data events, possible correlation. report

FIG. 22 shows a more detailed example of similarity analysis inaccordance with an exemplary embodiment. Here, the SimilarityAgentincludes a trained classifier that compares the details of a new eventto details of past events (shown in the figure as “List Testing Events”obtained from https://data.louisvilleky.gov/) and identifies relatedevents that are considered by the trained classifier to have asimilarity of at least 80%.

An EventClassificationAgent can be included to define event agency,type, priority or urgency based on the event description and otheravailable data. Here, an automatic classifier is trained with historicaldata (e.g., a pre-classified set of events). Feedback can be used by theEventClassificationAgent to learn from mistakes and improveclassification with time. Without limitation, some exemplaryapplications supported by the EventClassificationAgent include speedingup the call taking process, selecting an agency in a multiagency site,defining urgency on incoming messages, detecting sentiment on incomingmessages, and Automatic Event Creation (pre-creation).

FIG. 23 shows the architecture of the EventClassificationAgent inaccordance with an exemplary embodiment.

This exemplary embodiment uses the following Bayesian Classifier(conditional probability):P(A|B)=P(B|A)P(A)/P(B)  Bayes Theorem

More specification, Event Classification is based on the following:

Probability of a Class (C) given a Text (T)

Probability of a Class (C) given the Words (Wi) of the Text (T)P(C|T)=P(T|C)P(T)/P(C)P(C|T)=P(W1|C)*P(W2|C)* . . . P(T)/P(C)

Training=estimate the P(Wi|C)=create a bag of words for each class.

Testing=calculate P(C|T) for each class, and sort the result by theprobability.

Constant Learning=review the bag of words and statistics.

FIG. 24 shows a detailed example of event classification.

FIG. 25 shows another example of event classification.

The EventClassificationAgent can include voice-to-text integration forautomatically classifying events received through a voice-based system.FIG. 26 shows the architecture of voice-to-text integration inaccordance with an exemplary embodiment.

A ClusteringAnalysisAgent can be included to identify clusters ofevents. Without limitation, some exemplary applications include optimalpositioning of service centers (e.g., help place service centers toimprove response time; can be applied to patrol cars, rescue andemergency vehicles; minimize the distance between events and the servicecenters), finding patterns among the events (e.g., find regions in thedata where event density has a given characteristic; identify andevaluate the spread of events on the map, locate regions where a type ofevent is more prevalent), and monitoring of endemic phenomena (e.g.,diseases like Dengue and Zika are usually related to presence ofstanding water, and the constant monitoring of the formation of acluster of events can be an early indicator of this type of endemics).

FIG. 27 shows an example of how the ClusteringAnalysisAgent can helpidentify the best location for placement of tow trucks based on ahistory of towing events.

Similar techniques can be applied to plan EMS, Police units, utilities,fire brigades, maintenance units, and other ICAD-like resources with asimilar approach.

A RouteOptimizationAgent can be included to analyze a given set ofevents and to propose a route to visit the maximum number of events. TheAgent can continuously optimize the routes and suggest the bestallocation of a particular unit. The dispatcher can accept the proposaland assign the events to a unit manually. Without limitation, routeoptimization can be used to minimize the route in terms of distance ortime (e.g., to save in time on travel), maximize the number of eventsthat fit in a shift (e.g., 6 hours), start and end at a given location(e.g., base station), schedule the events to take an average time to beexecuted according to the type of the event. Route optimization canconsider the time to move from one event to another such as fromtraffic, if available.

To put the route optimization problem into perspective, suppose thereare 30 tasks to do but only 10 can be done. The problem then is how toselect the best 10 tasks to be done in a selected order. The number ofpossibilities=30×29×28×27×26×25×24×23×22×21=109 billion alternatives.Even if it took 0.0001 seconds to evaluate each possibility, it wouldtake 345 years to select the best set of 10. An alternative approachmight be to always select the closest one (e.g., could just select thefirst 10), but that may not result in the best or most important 10being done. Therefore, an exemplary embodiment applies a geneticalgorithm such as discussed herein to select an optimal route. Withoutlimitation, some algorithm considerations include maximizing theimportance of the events or its associated income; maximizing the numberof executed events if all events are equally important, minimizing theroute transportation cost, minimizing the resource costs by using theleast expensive resources, scheduling the execution of events to end ator near a given location, limiting the amount of time to execute theevents to a given shift, skipping events for which there are notavailable resources, considering traffic and other information affectingroutes, considering fees and service level agreement (SLA), and modelingunit and event type restrictions.

FIG. 28 shows an example graphical user interface screen showing anoptimized route.

A TimeEstimationAgent can be included to provide time estimates for suchthings as estimated time of arrival (ETA) and time to complete an event.Time estimation can be based on a specialized regression (e.g., DNN)based on such things as, without limitation, hour of the day, type ofevent, unit type, day of week, location information (e.g., origin and/ordestination), and other available/relevant information (e.g., weather,traffic, etc.), as represented in FIG. 29 .

Historic events can be used to train the regression service, which canbe updated constantly to improve time estimation. The dispatcher isnotified with a better time estimation.

An AreaCoverageAgent can be included to make location and relocationsuggestions for positioning field units such as for responding quicklyto future events based on such things number of available units, maximumtime/radius to reach the events, and expected redundancy on coverage.Future events can be predicted based on trends, historic data andexternal related information. In turn, the locations where units aremost likely to be needed can be predicted. Machine learning is used hereto help find optimal and near-optimal solutions such as to maximizecoverage and redundancy and reduce response times. The AreaCoverageAgentcan use clustering analysis, route optimization analysis, and timeestimation analysis as part of the location/relocation determinations.Outputs can include station locations and distribution of units amongthe station locations.

FIG. 30 shows an example graphical user interface screen showing stagedlocations for various units. The problem can be modeled as a maximumcovering location problem, e.g., given a facility associated with anumber of units and a radius R covered by the facility, maximize thenumber of units each event/demand is covered by and minimize the numberof units on a facility.

FIG. 31 shows an example coverage criteria definition. In this example,Facility F1 covers demands: d3, d4, d6 (3 demands) with one unit, whileFacility F2 covers demands: d1, d2, d3, d4, d5, d7 with two units. Inthis example, there are different qualities of service, e.g., Events d3and d4 are covered by 3 units; Events d1, d2, d5, and d7 are covered by2 units; and Event d6 is covered by 1 unit.

Demand can be represented by discrete points in the space. Facilitiesare located in selected demand points. Each facility can have one ormore units. Every demand is covered at least by one unit (except ifthere are not enough resources). The units are shared by all demandswithin the reach Radius of that facility. The agent can consider alinear distance (Euler) or Manhattan distance. Reaching a demand meansreaching the demand center (not its radius for simplification of themodel). This approach tends to place more units where there are moreevents and limits the number of redundant units to a demand as a qualityof service index. The number and location of the facilities are outputsof the algorithm. During location, the algorithm can include/exclude newfacilities. During relocation, the number and location of the facilitiesgenerally are fixed. Every time a relocation is performed, someimprovements in the distribution of the units can be proposed.

In an exemplary embodiment, inputs to the algorithm includeDemand=vector of N positions (X, Y, Events), Radius R, Desired Units perDemand (e.g. Max U=3), and Number of Units P. Location outputs from thealgorithm can include vector of Facilities that maximizes the demandcoverage, Number of units per facility, and Coverage List of thedemands. Relocation outputs from the algorithm can include Relocation ofunits to keep maximum demand coverage and Coverage List of the demands.

FIG. 32 shows an example location redundancy detail screen.

FIG. 33 shows an example of area coverage relocation.

Other contemplated Intelligent Agents include a safety agent thatanalyzes data to identify safety issues or improvements (e.g., when orunder what conditions a police officer is most likely to be injured), awell-being agent that analyzes personnel data (e.g., work time, resttime, physiological parameters, etc.) to identify exhaustion or stress,a workload agent that analyzes data to identify workload issues orimprovements including scheduling, a unit recommendation agent thatanalyzes data to recommend the most appropriate unit for a given event,natural language and social stream analysis agents to generatecontext-based notifications or provide inputs to other agents,predictive policing agents that analyze data to identify potentialcriminal activity and make recommendations for proactive and reactiveresponses, and various automation agents such as for making automaticassignment and dispatch decisions.

FIGS. 34-38 shows some exemplary graphical user interface screens inaccordance with one exemplary CAD system embodiment. FIG. 34 shows asample CAD system screen showing various types of notificationssuperimposed on a map. FIG. 35 shows some sample notifications ingreater detail. Here, the sample notifications include notificationsfrom a SimilarityAgent, a PatternAgent, and a KeywordAgent. The user canclick on a notification in order to view details of the notification.FIG. 36 shows a sample similarity notification detail indicating thesimilarity threshold determination between to events (i.e., 81.6%similar) and the top words matching the similarity (i.e., PRIVATE,PROPERTY). FIG. 37 shows a sample pattern notification detail indicatingthat a pattern was found based on a phone number and providing the eventdetails and a suggested action. FIG. 38 shows a sample keywordnotification detail indicating the keyword that was identified in theevent and providing the event details and a suggested action.

The following table shows the type(s) of algorithms used in the variousIntelligent Agents, in accordance with one exemplary embodiment:

Algorithm Agents Rule-Based Logic RuleAgent Fuzzy Logic EventMatchAgent,RepeatEventAgent Basic Statistics StatisticAgent Correlation StatisticsCorrelationAgent NLP—Natural Language PatternAgent, KeywordAgentProcessing Text Pattern Identification PatternAgent KeywordIdentification KeywordAgent Notification Likelihood AnalysisNotification Feedback Bayesian Classifier EventClassificationAgent,Notification Feedback Event Pattern Analysis EventClassificationAgentRepeated Event Identification RepeatEventAgent Event SimilaritySimilarityAgent Cosine Text Similarity SimilarityAgent TF-IDF AlgorithmSimilarityAgent Genetic Algorithm RouteOptimizationAgent,AreaCoverageAgent

FIGS. 41-51 show class models for the SmartAdvisor and variousIntelligent Agents, in accordance with one exemplary embodiment.

MISCELLANEOUS

It should be noted that headings are used above for convenience and arenot to be construed as limiting the present invention in any way.

Various embodiments of the invention may be implemented at least in partin any conventional computer programming language. For example, someembodiments may be implemented in a procedural programming language(e.g., “C”), or in an object-oriented programming language (e.g.,“C++”). Other embodiments of the invention may be implemented as apre-configured, stand-along hardware element and/or as preprogrammedhardware elements (e.g., application specific integrated circuits,FPGAs, and digital signal processors), or other related components.

In an alternative embodiment, the disclosed apparatus and methods (e.g.,see the various flow charts described above) may be implemented as acomputer program product for use with a computer system. Suchimplementation may include a series of computer instructions fixedeither on a tangible, non-transitory medium, such as a computer readablemedium (e.g., a diskette, CD-ROM, ROM, or fixed disk). The series ofcomputer instructions can embody all or part of the functionalitypreviously described herein with respect to the system.

Those skilled in the art should appreciate that such computerinstructions can be written in a number of programming languages for usewith many computer architectures or operating systems. Furthermore, suchinstructions may be stored in any memory device, such as semiconductor,magnetic, optical or other memory devices, and may be transmitted usingany communications technology, such as optical, infrared, microwave, orother transmission technologies.

Among other ways, such a computer program product may be distributed asa removable medium with accompanying printed or electronic documentation(e.g., shrink wrapped software), preloaded with a computer system (e.g.,on system ROM or fixed disk), or distributed from a server or electronicbulletin board over the network (e.g., the Internet or World Wide Web).In fact, some embodiments may be implemented in a software-as-a-servicemodel (“SAAS”) or cloud computing model. Of course, some embodiments ofthe invention may be implemented as a combination of both software(e.g., a computer program product) and hardware. Still other embodimentsof the invention are implemented as entirely hardware, or entirelysoftware.

Computer program logic implementing all or part of the functionalitypreviously described herein may be executed at different times on asingle processor (e.g., concurrently) or may be executed at the same ordifferent times on multiple processors and may run under a singleoperating system process/thread or under different operating systemprocesses/threads. Thus, the term “computer process” refers generally tothe execution of a set of computer program instructions regardless ofwhether different computer processes are executed on the same ordifferent processors and regardless of whether different computerprocesses run under the same operating system process/thread ordifferent operating system processes/threads.

Importantly, it should be noted that embodiments of the presentinvention may employ conventional components such as conventionalcomputers (e.g., off-the-shelf PCs, mainframes, microprocessors),conventional programmable logic devices (e.g., off-the shelf FPGAs orPLDs), conventional hardware components (e.g., off-the-shelf ASICs ordiscrete hardware components), and conventional AI/ML algorithms which,when programmed or configured to perform the non-conventional methodsdescribed herein, produce non-conventional devices or systems. Thus,there is nothing conventional about the inventions described hereinbecause even when embodiments are implemented using conventionalcomponents, the resulting devices and systems (e.g., the CAD system withIntelligent Agents and intelligent notifications subsystem) arenecessarily non-conventional because, absent special programming orconfiguration, the conventional components do not inherently perform thedescribed non-conventional functions.

The activities described and claimed herein provide technologicalsolutions to problems that arise squarely in the realm of technology.These solutions as a whole are not well-understood, routine, orconventional and in any case provide practical applications thattransform and improve computers and computer routing systems.

While various inventive embodiments have been described and illustratedherein, those of ordinary skill in the art will readily envision avariety of other means and/or structures for performing the functionand/or obtaining the results and/or one or more of the advantagesdescribed herein, and each of such variations and/or modifications isdeemed to be within the scope of the inventive embodiments describedherein. More generally, those skilled in the art will readily appreciatethat all parameters, dimensions, materials, and configurations describedherein are meant to be exemplary and that the actual parameters,dimensions, materials, and/or configurations will depend upon thespecific application or applications for which the inventive teachingsis/are used. Those skilled in the art will recognize, or be able toascertain using no more than routine experimentation, many equivalentsto the specific inventive embodiments described herein. It is,therefore, to be understood that the foregoing embodiments are presentedby way of example only and that, within the scope of the appended claimsand equivalents thereto, inventive embodiments may be practicedotherwise than as specifically described and claimed. Inventiveembodiments of the present disclosure are directed to each individualfeature, system, article, material, kit, and/or method described herein.In addition, any combination of two or more such features, systems,articles, materials, kits, and/or methods, if such features, systems,articles, materials, kits, and/or methods are not mutually inconsistent,is included within the inventive scope of the present disclosure.

Various inventive concepts may be embodied as one or more methods, ofwhich examples have been provided. The acts performed as part of themethod may be ordered in any suitable way. Accordingly, embodiments maybe constructed in which acts are performed in an order different thanillustrated, which may include performing some acts simultaneously, eventhough shown as sequential acts in illustrative embodiments.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the claims, “consisting of,” will refer to the inclusion of exactlyone element of a number or list of elements. In general, the term “or”as used herein shall only be interpreted as indicating exclusivealternatives (i.e., “one or the other but not both”) when preceded byterms of exclusivity, such as “either,” “one of,” “only one of,” or“exactly one of.” “Consisting essentially of,” when used in the claims,shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively, as set forth in the United States Patent Office Manual ofPatent Examining Procedures, Section 2111.03.

Although the above discussion discloses various exemplary embodiments ofthe invention, it should be apparent that those skilled in the art canmake various modifications that will achieve some of the advantages ofthe invention without departing from the true scope of the invention.Any references to the “invention” are intended to refer to exemplaryembodiments of the invention and should not be construed to refer to allembodiments of the invention unless the context otherwise requires. Thedescribed embodiments are to be considered in all respects only asillustrative and not restrictive.

What is claimed is:
 1. A computer-aided dispatch (CAD) systemcomprising: a CAD database storing CAD data; and at least one servercomprising a tangible, non-transitory computer readable medium havingstored thereon an agent hoster subsystem and a notification subsystem,wherein: the agent hoster subsystem is configured to communicate with aplurality of Intelligent Agents, each Intelligent Agent configured toperform a distinct dispatch-related analysis of the CAD data and toproduce dispatch-related notifications based on such analysisautonomously without being queried; and the notification subsystem isconfigured to implement a machine learning filter that usescharacteristics of previous notifications provided to a given user anduser feedback associated with such previous notifications to classifynotifications and determine which types of notifications the given userwants to receive and which types of notifications the given user doesnot want to receive and to selectively present future notifications tothe given user based at least in part on the determination of whichtypes of notifications the given user wants to receive and which typesof notifications the given user does not want to receive, wherein thenotification system is configured to present wanted notifications alongwith a small percent of unwanted notifications to the given user via anotification interface so that the given user can provide additionalfeedback on such unwanted notifications to indicate whether the givenuser wants to receive such unwanted notifications in the future, andwherein the machine learning filter is updated based on such additionalfeedback for selectively presenting future notifications.
 2. The systemaccording to claim 1, wherein notifications presented to the given userprovide a mechanism for the given user to provide feedback regardingvalue of the notification to the given user.
 3. The system according toclaim 2, wherein the feedback comprises a like/dislike indication. 4.The system according to claim 2, wherein the feedback comprising a valuerating.
 5. The system according to claim 1, wherein the machine learningfilter implements a Bayesian classifier to classify notifications. 6.The system according to claim 1, wherein the notification system furtheruses pre-defined user preferences to determine which types ofnotifications the given user wants to receive.
 7. The system accordingto claim 1, wherein the notification system is configured to learnnotification preferences separately for a plurality of users.
 8. Thesystem according to claim 1, wherein the number of definitively unwantednotifications presented to the user is configurable by an administrator.9. The system according to claim 1, wherein the notification system isconfigured to associate a number of related notifications and toselectively present either all of the related notifications or none ofthe related notifications.
 10. The system according to claim 1, whereinat least one of the Intelligent Agents is a machine-learning Intelligentagent that is retrained based on the user feedback.
 11. A computerprogram product comprising a tangible, non-transitory computer readablemedium having embodied therein a computer program which, when run on acomputer, implements computer processes comprising: an agent hostersubsystem configured to communicate with a plurality of IntelligentAgents, each Intelligent Agent configured to perform a distinct dispatchrelated analysis of computer-aided dispatch (CAD) data and to producedispatch-related notifications based on such analysis autonomouslywithout being queried; and a notification subsystem configured toimplement a machine learning filter that uses characteristics ofprevious notifications provided to a given user and user feedbackassociated with such previous notifications to classify notificationsand determine which types of notifications the given user wants toreceive and which types of notifications the given user does not want toreceive and to selectively present future notifications to the givenuser based at least in part on the determination of which types ofnotifications the given user wants to receive and which types ofnotifications the given user does not want to receive, wherein thenotification system is configured to present wanted notifications alongwith a small percent of unwanted notifications to the given user via anotification interface so that the given user can provide additionalfeedback on such unwanted notifications to indicate whether the givenuser wants to receive such unwanted notifications in the future, andwherein the machine learning filter is updated based on such additionalfeedback for selectively presenting future notifications.
 12. Thecomputer program product according to claim 11, wherein notificationspresented to the given user provide a mechanism for the given user toprovide feedback regarding value of the notification to the given user.13. The computer program product according to claim 12, wherein thefeedback comprises a like/dislike indication.
 14. The computer programproduct according to claim 12, wherein the feedback comprises a valuerating.
 15. The computer program product according to claim 11, whereinthe machine learning filter implements a Bayesian classifier to classifynotifications.
 16. The computer program product according to claim 11,wherein the notification system further uses pre-defined userpreferences to determine which types of notifications the given userwants to receive.
 17. The computer program product according to claim11, wherein the notification system is configured to learn notificationpreferences separately for a plurality of users.
 18. The computerprogram product according to claim 11, wherein the number ofdefinitively unwanted notifications presented to the user isconfigurable by an administrator.
 19. The computer program productaccording to claim 11, wherein the notification system is configured toassociate a number of related notifications and to selectively presenteither all of the related notifications or none of the relatednotifications.
 20. The computer program product according to claim 11,wherein at least one of the Intelligent Agents is a machine-learningIntelligent agent that is retrained based on the user feedback.